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**Takeaways**

- Bayesian methods align better with researchers’ intuitive understanding of research questions and provide more tools to evaluate and understand models.
- Prior sensitivity analysis is crucial for understanding the robustness of findings to changes in priors and helps in contextualizing research findings.
- Bayesian methods offer an elegant and efficient way to handle missing data in longitudinal studies, providing more flexibility and information for researchers.
- Fit indices in Bayesian model selection are effective in detecting underfitting but may struggle to detect overfitting, highlighting the need for caution in model complexity.
- Bayesian methods have the potential to revolutionize educational research by addressing the challenges of small samples, complex nesting structures, and longitudinal data.
- Posterior predictive checks are valuable for model evaluation and selection.

**Chapters**

00:00 The Power and Importance of Priors

09:29 Updating Beliefs and Choosing Reasonable Priors

16:08 Assessing Robustness with Prior Sensitivity Analysis

34:53 Aligning Bayesian Methods with Researchers’ Thinking

37:10 Detecting Overfitting in SEM

43:48 Evaluating Model Fit with Posterior Predictive Checks

47:44 Teaching Bayesian Methods

54:07 Future Developments in Bayesian Statistics

**Thank you to my Patrons for making this episode possible!**

*Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev,* *Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan and Francesco Madrisotti*.

**Links from the show**

- Sonja’s website: https://winterstat.github.io/
- Sonja on Twitter: https://twitter.com/winterstat
- Sonja on GitHub: https://github.com/winterstat
- Under-Fitting and Over-Fitting – The Performance of Bayesian Model Selection and Fit Indices in SEM: https://www.tandfonline.com/doi/full/10.1080/10705511.2023.2280952
- LBS #102 – Bayesian Structural Equation Modeling & Causal Inference in Psychometrics, with Ed Merkle: https://youtu.be/lXd-qstzTh4?si=jLg_qZTt1oQqRO0R
- LBS #107 – Amortized Bayesian Inference with Deep Neural Networks, with Marvin Schmitt: https://learnbayesstats.com/episode/107-amortized-bayesian-inference-deep-neural-networks-marvin-schmitt/
- BayesFlow tutorial: https://bayesflow.org/_examples/Intro_Amortized_Posterior_Estimation.html
- LBS #106 Active Statistics, Two Truths & a Lie, with Andrew Gelman: https://learnbayesstats.com/episode/106-active-statistics-two-truths-a-lie-andrew-gelman/
- LBS #61 Why we still use non-Bayesian methods, with EJ Wagenmakers: https://learnbayesstats.com/episode/61-why-we-still-use-non-bayesian-methods-ej-wagenmakers/
- Bayesian Workflow paper: https://arxiv.org/abs/2011.01808
- Michael Betancourts’blog: https://betanalpha.github.io/writing/
- LBS #35 The Past, Present & Future of BRMS, with Paul Bürkner: https://learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner/
- Bayesian Model-Building Interface in Python: https://bambinos.github.io/bambi/
- Advanced Regression online course: https://www.intuitivebayes.com/advanced-regression
- BLIMP: https://www.appliedmissingdata.com/blimp

**Transcript**

*This is an automatic transcript and may therefore contain errors. Please **get in touch** if you’re willing to correct them.*

##### Transcript

Priors represent a crucial part of the

Bayesian workflow, and actually a big

2

reason for its power and usefulness.

3

But why is that?

4

How do you choose reasonable priors in

your models?

5

What even is a reasonable prior?

6

These are deep questions that today's

guest, Sonja Winter, will guide us

7

through.

8

an assistant professor in the College of

Education and Human Development of the

9

University of Missouri, Sonia's research

focuses on the development and application

10

of patient approaches to the analysis of

educational and developmental

11

psychological data, with a specific

emphasis on the role of priors.

12

What a coincidence!

13

In this episode, she shares insights on

the selection of priors, prior sensitivity

14

analysis, and the challenges of working

15

with longitudinal data.

16

She also explores the implications of

Bayesian methods for model selection and

17

fit indices in structural equation

modeling, as well as the challenges of

18

detecting overfitting in models.

19

When she's not working, you'll find Sonja

baking delicious treats, gardening, or

20

watching beautiful birds.

21

This is Learning Bayesian Statistics,

episode.

22

Welcome to Learning Bayesian Statistics, a

podcast about Bayesian inference, the

23

methods, the projects, and the people who

make it possible.

24

I'm your host, Alex Andorra.

25

You can follow me on Twitter at alex

.andorra, like the country.

26

For any info about the show, learnbasedats

.com is Laplace to be.

27

Show notes, becoming a corporate sponsor,

unlocking Bayesian Merge, supporting the

28

show on Patreon, everything is in there.

29

That's learnbasedats .com.

30

If you're interested in one -on -one

mentorship, online courses, or statistical

31

consulting, feel free to reach out and

book a call.

32

at topmate .io slash alex underscore and

dora see you around folks and best patient

33

wishes to you all.

34

Hello my dear patients, today I want to

thank the fantastic Jonathan Morgan and

35

Francesco Madrisotti for supporting the

show on Patreon.

36

Your support is invaluable and literally

makes this show possible.

37

can't wait to talk with you guys in the

Slack channel.

38

Second, with my friends and fellow PymC

core developers, Ravin Kumar and Tommy

39

Capretto, we've just released our new

online course, Advanced Regression with

40

Bambi and PymC.

41

And honestly, after two years of

development, it feels really great to get

42

these out into the world, not only because

it was, well, long and intense, but mainly

43

because I am so proud of the level of

44

of details, teachings, and exercises that

we've packed into this one.

45

It's basically the course I wish I had

once I had gone through the beginner's

46

phase when learning patience tests, that

moment when you're like...

47

Okay, I know how to do basic models, but

where do I go from here?

48

I remember feeling quite lost, so we

wanted to give you a one -stop shop for

49

such intermediate models with the most

content possible, as evergreen as it gets.

50

If that sounds interesting, go to

intuitivebase .com and check out the full

51

syllabus.

52

We're enrolling the first cohort as we

speak!

53

Of course, you get a 10 % discount if

you're a patron of the show.

54

Go to the Patreon page or the Slack

channel to get the code.

55

Okay, back to the show now and looking

forward to seeing you in the intuitive

56

base discourse.

57

Sonia Winter, welcome to Learning Bayesian

Statistics.

58

Thank you.

59

Thanks for having me.

60

I'm really excited to talk to you today.

61

Same, same.

62

That's a treat.

63

I have a lot of questions.

64

I really love.

65

like everything you're doing in your

research.

66

We're going to talk a lot about priors

today, folks.

67

So yeah, like get ready.

68

But first, can you provide a brief

overview of your research interests and

69

how patient methods play a role in your

work?

70

Yeah, sure.

71

So my background is actually in

developmental psychology.

72

I did a bachelor or master's degree at

Utrecht University.

73

And during that time, I really realized

that a lot of work needed to be done on

74

the analysis part of social science

research.

75

And so I switched and got really into

structural equation models, which are

76

these big multivariate models that include

latent variables.

77

I'm sure we'll talk more about that later.

78

But those models can be hard to estimate

and there are all these issues.

79

And so I was introduced to Bayesian

statistics.

80

right after my master's degree when I was

working with Rens van der Schoot, also at

81

Utrecht University.

82

And he asked me to do this big literature

review about it with him.

83

And that really introduced me.

84

And so now I focus a lot on Bayesian

estimation and how it can help us estimate

85

these structural equation models.

86

And then specifically more recently, I've

really been focusing on how those priors

87

can really help us.

88

both with estimation and also just with

understanding our models a little bit

89

better.

90

So yeah, I'm really excited about all of

that.

91

Yeah, I can guess that sounds awesome.

92

So structural equation modeling, we

already talked about it on the show.

93

So today we're going to focus a bit more

on priors and how that fits into the SEM

94

framework.

95

So for people who don't know about SEM, I

definitely recommend episode 102 with Ed

96

Merkel.

97

And we talked exactly about structural

equation modeling and causal inference in

98

psychometrics.

99

So that will be a.

100

a very good introduction i think to these

topics for people and what i'm curious

101

about sonia is you work a lot on priors

and things like that but how how did you

102

end up working on that was something that

you were always curious about or that

103

something that appeared later later on in

your in your phd studies

104

I would say definitely something that

started or piqued my interest a little bit

105

later.

106

I think so after I first got familiarized

with Bayesian methods, I was excited

107

mostly by how it could help, like priors

could help us estimate, like avoid

108

negative variances and those types of

things.

109

But I saw them more as a pragmatic tool to

help with that.

110

And I didn't really focus so much on that.

111

I feel like I also was a little bit afraid

at the time of, you know, those

112

researchers who talk a lot about, well, we

shouldn't really make our priors

113

informative because that's subjective and

that's bad.

114

And so I really typically use like

uninformative priors or like software

115

defaults for a lot of my work in the

beginning.

116

But then during my PhD studies, I

actually.

117

Well, first of all, I worked with another

researcher, Sanaa Smith, who was also a

118

PhD student at the time.

119

And she was really intrigued by something

she found that these software defaults can

120

really cause issues when you're,

especially when your data is like very

121

small, it can, it can make your results

look wild.

122

And so we worked on this paper together

and created a shiny app to demonstrate all

123

of that.

124

And that made me realize that maybe

uninformative priors.

125

are not always the best way to go.

126

And also a prior that looks informative in

one scenario might be relatively

127

uninformative in another.

128

And so I really started shifting my, my

perspective on priors and focusing more on

129

how ignoring them is kind of like ignoring

the best part of Bayesian in my opinion,

130

at this point.

131

and so now I really want to look at how,

how they can help us and how we can be

132

thoughtful.

133

We don't want to drive our science by

priors, right?

134

We want to learn something new from our

data, but we find that balance is really

135

what I'm looking for now.

136

Yeah, well, what a fantastic application

of updating your belief, right?

137

From a meta standpoint, you just like

updated your priors pretty aggressively

138

and also very rationally.

139

That's really impressive.

140

Well done.

141

Because that's hard to do also.

142

It's not something we like to do.

143

So that's great.

144

Well done on doing that.

145

And actually now that you're on the other

side, how do you approach the selection of

146

priors in your research and what advice do

you have for people new to Bayesian

147

methods?

148

Yeah, great question.

149

I think at least within structural

equation modeling, we as like applied

150

researchers are helped somewhat because

distributions, at least for priors, are

151

sort of clear.

152

Like you don't have to think too much

about them.

153

And so you can immediately jump into

thinking about, okay, what level of

154

information do I want to convey in those

priors?

155

And I think whenever I'm working with

applied researchers, I try to strike a

156

balance with them because I know they are

not typically comfortable using like super

157

informative priors that are really narrow.

158

And so I just asked them to think about,

well, what would be a reasonable range?

159

Like if we are estimating a linear

regression parameter, what would that

160

effect size look like?

161

Right.

162

It might be zero or it might be two, but

it's probably not going to be 20.

163

And so we can.

164

sort of shape our prior to align with

those sort of expectations about how

165

probable certain values are versus others.

166

It's a really, I don't know, interactive

process between me and the researcher to

167

get this right, especially for those types

of parameters that they are really

168

interested in.

169

I think another type of parameter that is

more challenging for applied researchers

170

are those that are placed on residual

variances, for example.

171

Like people typically don't...

172

think about the part of the outcome that

they can't explain that much.

173

And so that's where I do rely a bit more

on sort of, I don't know, industry

174

standard choices that are typically not

super informative.

175

But then once we pick our like target

priors, I always advise the researcher to

176

follow it up with a sensitivity analysis

to see.

177

like how robust their findings are to

changes in those priorities, either making

178

them more informative or less informative.

179

And so yeah, that's really the approach I

take.

180

Of course, if someone wants to go full

base and full informative and they have

181

this, this wealth of previous research to

draw from, then I'm all for going, going

182

that route as well.

183

It's just not as common.

184

Hmm.

185

Hmm.

186

Yeah, I see.

187

in what, what are the...

188

main difficulties you see from people that

you advise like that?

189

Where do you see them having more defenses

up or just more difficulties because they

190

have a hard time wrapping their head

around a specific concept?

191

I think just all over, I think if anyone

has ever tried to do like a power analysis

192

working with researchers, it's sort of a

similar concept because

193

It is not, at least in my field or the

people I work with are not very typically

194

already thinking about the exact parameter

estimates that they are expecting to see,

195

right?

196

They are just, they just go with the

hypothesis.

197

I think these two things are correlated

and they might not even go as far as to

198

think, is it positive or negative?

199

So then once you ask them those questions,

it really forces them to go much deeper on

200

their theory and really consider like.

201

What is, what am I expecting?

202

What is reasonable based on what I know

from, from previous studies or just

203

experience.

204

And that can be kind of challenging.

205

It's, it's kind of, I think sometimes the

researchers might feel like I'm

206

criticizing them for not knowing, but I

think that's perfectly normal to not know.

207

Like we already have so many other things

to think about.

208

But it definitely.

209

is kind of a hurdle.

210

Also the time commitment, I think, to

really consider the priors, especially if

211

you're coming from a frequentist realm

where you just say, okay, maximum

212

likelihood go.

213

Not only do you not have to think about

the estimation, but then also your results

214

are almost instant.

215

And so that's always kind of a challenge

as well.

216

I see.

217

Yeah.

218

Yeah.

219

Definitely something also I seen, I seen

beginners.

220

yeah, it, it really depends on also where

they are coming from, as you were saying.

221

Yeah.

222

I did.

223

Your advice will depend a lot on that.

224

yeah.

225

Yeah.

226

And actually you work also a lot on prior

sensitivity analysis.

227

can you, can you tell people what that is?

228

And the importance of it in your, in your

modeling workflow and.

229

how you incorporate it into your research.

230

Yeah.

231

So a sensitivity analysis for priors is

something that you typically do after you

232

run your main analysis.

233

So you come up with your target set of

priors for all your parameters, estimate

234

the model, look at the results, look at

the posteriors.

235

And then in the next step, you think

about, well, how can I change these

236

priors?

237

in sort of meaningful ways, either making

them more informative, perhaps making them

238

represent some other theory, making them

less informative as well.

239

So making the influence of the prior

weaker in your results.

240

And then you rerun your analysis for all

of those different prior scenarios, and

241

then compare those results to the ones

that you actually obtained with your

242

target analysis and your target priors.

243

And the idea here is to see,

244

how much your results actually depend on

those prior beliefs that you came into the

245

analysis with.

246

If you don't find any differences, then

you can say, well, my results are mostly

247

influenced by my data, by the new evidence

that I obtained.

248

They are robust to changes in prior

beliefs, right?

249

It doesn't really matter what beliefs you

came into the analysis with.

250

The results are going to be the same,

which is great.

251

In other cases, you might find that your

results do change meaningfully.

252

So for example, in effect that was

significant with your priors is no longer

253

significant using a frequentist term here,

but hopefully people will understand once

254

you change your priors.

255

And that's, of course, is a little bit

more difficult to handle because what do

256

you do?

257

I want to say that the goal is not to

258

use the sensitivity analysis to then go

back and change your priors and run the

259

analysis again and report that in your

paper.

260

That would be sort of akin to p -hacking.

261

Instead, I think it just contextualizes

your findings.

262

It's showing that the knowledge you came

into the analysis with is partially

263

driving your results.

264

And that probably means that the evidence

in your new data is not super strong.

265

And so it may indicate some issues with

your theory or some issues with your data.

266

And you have to collect more data to

figure out which of those it is basically.

267

And so it's, it's kind of helping you also

figure out the next steps in your

268

research, I feel, which is helpful.

269

But it can be frustrating, of course, and

harder to convince maybe co -authors and

270

reviewers to.

271

move forward with a paper like that.

272

But to me it is very interesting these

results from sensitivity analyses.

273

Yeah, yeah, completely agree in that.

274

That's very interesting to see the, yeah,

if the results differ on the priors, and

275

that can also help, you know, settle any

argument on the choice of prior.

276

You know, if people are really in

disagreement about which priors to choose,

277

well, then you can run the model with both

sets of priors, and if the results don't

278

change, it's like, well, let's stop

arguing.

279

That's kind of...

280

It's kind of silly.

281

We just lost time.

282

So let's just focus on the results then.

283

I think it's a very interesting framework.

284

And then there is another.

285

So that is like that entails running the

model, running MCMC on the model.

286

But there are some checks that you do

before that to ensure the robustness of

287

your patient models.

288

And one of that step is.

289

very crucial and called primary predictive

checks.

290

Can you talk about that to beat Sonja?

291

Yeah, so as you said, these checks happen

before you do any actual analysis.

292

So you can do them before you collect any

data.

293

In fact, one reason for using them is to

figure out whether the priors you came up

294

with results in sensible ranges of

possible parameter estimates, right?

295

In some cases, especially with these

complex multivariate models, your priors

296

may interact in unexpected ways and then

result in predictions that are not in line

297

with what your theory is actually telling

you you should expect.

298

And so prior predictive checks basically

until you specify your priors for all your

299

parameters.

300

And then you generate

301

parameter values from those priors by

combining it with your model

302

specification.

303

And then those combinations of parameter

estimates are used to generate what are

304

called prior predictive samples.

305

So these are samples of some pre

-specified size that represent possible

306

observations that align with what your

priors are conveying combined with your

307

model.

308

And so ideally,

309

those prior predictive samples look kind

of like what you would expect your data to

310

look like.

311

And sometimes for researchers, it is

easier to think about what the data should

312

look like compared to what the parameter

estimates can be.

313

And so in that sense, prior predictive

checks can be really helpful in checking

314

not just the priors, but checking the

researcher and making sure that they

315

actually convey their knowledge to me, for

example, correctly.

316

Yeah, did that answer your question?

317

Yeah, yeah, I think that's a great

definition and definitely encourage any

318

Bayesian practitioner to include prior

predictive checks in their workflow.

319

Once you have written a model, that should

be the first thing you do.

320

Do not run a CMC before doing prior

predictive checks.

321

And recently, I feel like a lot of the

software packages for Bayesian methods

322

have...

323

included very simple ways of running these

checks, which when I first started looking

324

at them, it was kind of more of a niche

step in the workflow.

325

And so it required a few more steps and

some more like coding, but now it's as

326

easy as just switching like a toggle to

get those prior predictive samples.

327

So that's great.

328

Yeah, yeah, yeah, completely agree.

329

That's also, yeah, it's definitely

something that's, that's been more and

330

more popular in the different

331

classes and courses that I teach, whether

it's online courses or live workshops,

332

always show prior predictive checks almost

all the time.

333

So yeah, it's becoming way, way more

popular and widespread.

334

So that's really good because I can tell

you when I work on a real model for

335

clients, always the first thing I do

before running MCMC is prior predictive

336

checks.

337

And actually there is a fantastic way

of...

338

you know, doing prior predictive checks,

like kind of industrialized and that's

339

called simulation based calibration.

340

Have you heard of that?

341

No, I mean, maybe the term, but I have no

idea what it is.

342

So that's just like making prior

predictive checks on an industrialized

343

scale.

344

Basically now instead of just

345

running through the model forward, as you

explained, and generate prior predictive

346

samples, what you're doing with SPC, so

simulation -based calibration, is

347

generating not only prior predictive

samples, but prior samples of the

348

parameters of the model.

349

You stock these parameters in some object.

350

but you don't give them to the model, but

you keep them somewhere safe.

351

And then the prior predictive samples, so

the plausible observations generated by

352

the model based on the prior samples that

you just kept in the fridge, these prior

353

predictive samples, now you're going to

consider them as data.

354

And you're going to tell the model, well,

run MCMC on these data.

355

as if we had observed these prior

predictive samples in the wild, because

356

that's what prior predictive samples are.

357

It's possible samples we could observe

before we know anything about real data.

358

So you feed that to the model.

359

You make the model run MCMC on that.

360

So that means backward inference.

361

So now the model is going to find out

about the plausible parameter values which

362

could have generated this data.

363

And then what you're going to do is

compare the posterior

364

distribution that the model inferred for

the parameter values to the true parameter

365

values that you kept in the fridge before.

366

You're going to get, so these parameter

values are true.

367

So you just have one of them, because it's

just one sample from the prior parameters.

368

And you're going to compare these value,

these value to the distribution of

369

posterior parameters that you just got

from the model.

370

And based on that,

371

and how far the model is from the true

parameter, you can find out if your model

372

is biased or if it's well calibrated.

373

And that's a really great way to be much

more certain that the model is able to

374

recover what you want it to recover.

375

basically playing God, and then you're

trying to see if the model is able to

376

recover the parameters that you use to

generate the data.

377

And not only will you do that once, but

you want to do that many times, many, many

378

times, because, well, the more you do it,

then you enter a kind of a frequentist

379

realm, right?

380

Where you're like, you just repeat the

experiments a lot.

381

And then that's how you're going to see

how calibrated the model is, because then

382

you can do some calibration plots.

383

there are a lot of metrics around that

it's a kind of a developing area of the

384

research but there are a lot of metrics

and one of them is basically just plotting

385

the true parameter values and well for

instance the mean posterior value from the

386

parameter and then if this mean is most of

the time along the the line x equals y

387

well that means you are in pretty good

shape you are but I mean it's the mean

388

here you

389

So you have to look at the whole

distribution, but that's to give you an

390

idea.

391

And so the bottleneck is you want to do

that a lot of time.

392

So you have to run MCMC a lot of times.

393

Most of the time, if you're just doing a

regression, that should be okay.

394

But sometimes it's going to take a lot of

time to run MCMC and it can be hard.

395

In these cases, you have new algorithms

that can be efficient because there is one

396

called

397

amortized Bayesian inference, a method

called amortized Bayesian inference.

398

We just covered that in episode 107 with

Marvin Schmidt.

399

And basically that's exactly a use case

for amortized Bayesian inference because

400

the model doesn't change, but the data

changes in each iteration of the loop.

401

And so what amortized Bayesian inference

is doing is just, well, just is training a

402

deep neural network on the model.

403

as a first step.

404

And then the second step is the inference,

but the inference is just instantaneous

405

because you've trained the deep neural

network.

406

And that means you can do, you can get as

black, almost as many poster samples as

407

you want.

408

Once you have trained the deep neural

network.

409

And so that's why it's almost all

inspection inference.

410

And that's a perfect use case for SBC

because then like you can just like you

411

get new, a new,

412

new samples for free.

413

And actually, so I definitely encourage

people to look at that.

414

It's still developing.

415

So right now you cannot, for instance, use

Baseflow, which is the Python package that

416

Marvin talked about in 1 .07 with PIMC,

but it's something we're working on.

417

And the goal is that it's completely

compatible.

418

But yeah, like I'll link to the tutorial

notebook in the show notes for people.

419

who want to get an idea of what SPC is

because even though you're not applying it

420

right now at least you have that in mind

and you know what that means and you can

421

work your way to out that.

422

Yeah that's amazing.

423

I feel like one of the biggest hurdles in

the structural equation modeling approach

424

with using Bayesian is just the time

commitment.

425

I'm

426

There is one analysis I was running and it

takes, I think for one analysis, it takes

427

almost a week to run it because it's a big

sample and then it's a complicated model.

428

And so if I would have to rerun that model

a thousand times, it would not be fun.

429

so knowing that there's maybe some options

on the horizon to help us speed along that

430

process would be, I think that would

change our field for sure.

431

So that's very exciting.

432

Yeah, yeah, yeah.

433

That's really super exciting.

434

And that's why I'm also super enthusiastic

about the desalmatized Bayesian infant

435

stuff, because I discovered that in

episode 107, so it's not a long time ago.

436

But as soon as I heard about that, I dug

into it, because that's super interesting.

437

Yeah.

438

I'm going to read about it after we finish

recording this.

439

Yeah, yeah, for sure.

440

And feel free to send me any questions.

441

And I find it's also a very elegant way to

marry the Bayesian framework in the deep

442

neural network methods.

443

So I really love that.

444

It's really elegant and promising, as you

were saying.

445

Talking about SCM, so structural equation

modeling, do you find that Bayesian

446

methods help?

447

in for these kind of models and especially

when it comes to educational research

448

which is one of your fields?

449

Yes, I think Bayesian methods can sort of

help on both ends of the spectrum that we

450

see with educational data which is either

we have very small samples and so

451

researchers still have these ambitious

theoretical models that they want to test.

452

but it's just not doable with frequentist

estimators.

453

And so based with the priors, it can help

a little bit to boost the information that

454

we have, which is really nice.

455

And then on the other side, ever since

starting this position and moving into a

456

college of education, I've been given

access to many large data sets that have

457

very complicated nesting structures.

458

That's something you see all the time in

education.

459

You have

460

schools and then teachers and students and

the students they change teachers because

461

it's also longitudinal so there's a time

component and all of these different

462

nested structures can be very hard to

model using estimators like nextman

463

likelihood and bayesian methods not

necessarily structural equation modeling

464

but maybe more a hierarchical linear model

or some other multi -level approach it can

465

be super flexible to handle all of those

466

structures and still give people results

that they can use to inform policy.

467

Because that's something in education that

I didn't really see when I was still in

468

the department of psychology before is

that a lot of the research here is really

469

directly informing what is actually going

to happen in schools.

470

And so it's really neat that these

Bayesian methods are allowing them to

471

answer much more complicated research

questions and really make use of all of

472

the data that they have.

473

So that's been really exciting.

474

And actually, I wanted to ask you

precisely what the challenges you face

475

with longitudinal data and how do you

address these challenges because I know

476

that can be pretty hard.

477

I think with longitudinal data, the

biggest challenge actually doesn't have

478

anything to do with the estimator.

479

It is more just inherent in longitudinal

data, which is that we will always...

480

unless you have a really special sample,

but we will always have missing data.

481

Participants will always drop out at some

point or just skip a measurement.

482

And of course, other estimation methods

also have options for accommodating

483

missing data, such as full information

maximum likelihood.

484

But I find that the Bayesian approach

where you can do imputation while you're

485

estimating, so you're just imputing the

data at every posterior sample, is very

486

elegant, efficient.

487

and easy for researchers to wrap their

minds around.

488

And it still allows you just like with

other multiple imputation methods to

489

include an sort of auxiliary model

explaining the missingness, which helps

490

with the like missing at random, type data

that we deal with a lot.

491

And so I feel that that is especially

exciting.

492

I honestly started thinking about this

more deeply when I started my position

493

here and I met my new colleague.

494

Dr.

495

Brian Keller, he is working on some

software, it's called BLIMP, which I think

496

it stands for Bayesian Latent Interaction

Modeling Program, I want to say.

497

So it's actually created for modeling

interactions between latent variables,

498

which is a whole other issue.

499

But within that software, they actually

also created a really powerful method for

500

dealing with missing data, or not

necessarily the method, but just the

501

application of it.

502

And so...

503

Now that I've met him and he's always

talking about it, it makes me think about

504

it more.

505

So that's very exciting.

506

Yeah, for sure.

507

And feel free to add a link to this

project to Blimp in the show notes,

508

because I think that's going to be very

interesting to listeners.

509

And how, I'm wondering if patient methods

improve...

510

the measurement and the evaluation

processes in educational settings, because

511

I know it's a challenge.

512

Is that something that you're working on

actively right now, or you've done any

513

projects on that that you want to talk

about?

514

Well, I teach measurement to grad

students.

515

So it's not necessarily that I get to talk

about Bayes a lot in there.

516

But what I'm realizing is that

517

When we talk about measurement from a

frequentist standpoint, we typically start

518

with asking students a bunch of questions.

519

Let's say we're trying to measure math

ability.

520

So we ask them a bunch of math questions.

521

Then if we use frequentist estimation, we

can use those item responses to generate

522

some sort of probability of those

responses giving some underlying level of

523

math ability.

524

So how probable is it that they gave these

answers given this level of math?

525

But actually what we want to know is what

is the student's math ability, given the

526

patterns of observed responses.

527

And so Bayes theorem gives us a really

elegant way of answering exactly that

528

question, right.

529

Instead of the opposite way.

530

And so I think in a big way, Bayesian

methods just align better with how people

531

already think about the research that

they're doing or the thing, the questions

532

that they're, they want to answer.

533

I think.

534

This is also a reason why a lot of

researchers struggle with getting the

535

interpretation of things like a confidence

interval correct, right?

536

It's just not intuitive.

537

Whereas Bayesian methods, they are

intuitive.

538

And so in that sense, I think not so much

like estimation wise, but just

539

interpretation wise, Bayesian methods can

help a lot in our field.

540

And then in addition to that, I think when

we do use Bayesian estimation,

541

those posterior distributions, they can

give us so much more information about the

542

parameters of interest that we are

interested in.

543

And they can also help us understand what

future data would look like given those

544

posteriors, right?

545

If we move from like prior predictors to

posterior predictors, which are these

546

samples generated from the posteriors,

that should look like our data should look

547

like that data, right?

548

If our model is doing a good job of

representing our data.

549

And so,

550

I think that's an exciting extension of

Bayes as well.

551

It gives us more tools to evaluate our

model and to make sure that it's actually

552

doing a good job of representing our data,

which is especially important in

553

structural equation modeling, where we

rely very heavily on global measures of

554

fit.

555

And so this is a really nice new tool for

people to use.

556

I see.

557

Okay.

558

Yeah.

559

I am.

560

I need to know about that in particular.

561

That's...

562

That's very interesting.

563

Yeah.

564

So I mean, I would have more questions on

that, but I want to ask you in particular

565

on a publication you have about under

-fitting and over -fitting.

566

And you've looked at the performance of

Bayesian model selection in SEM.

567

I find that super interesting.

568

So can you summarize the key findings of

this paper and...

569

their application, their implications for

researchers using SEM?

570

Yeah, for sure.

571

This is a really fun project for me to

work on, kind of an extension of my

572

dissertation.

573

So it made me feel like, I'm really moving

on, creating a program of research.

574

So yeah, thanks for asking about the

paper.

575

So yeah, as I already kind of mentioned,

within structural equation modeling,

576

Researchers rely really heavily on these

model selection and fit indices to make

577

choices about what model they're going to

keep in the end.

578

A lot of the times, researchers come in

with some idea of what the model would

579

look like, but they are always tinkering a

little bit.

580

They're ready to know that they're wrong

and they want to get to a better model.

581

And so the same is true when we use

Bayesian estimation and we have sort of a

582

similar set of indices to look at.

583

in terms of the fit of a single model or

comparing multiple models and selecting

584

the best one.

585

And so very typically those indices are

tested in terms of how well they can

586

identify underfit.

587

And so underfit occurs when you forgot to

include a certain parameter.

588

So your model is too simple for the

underlying data generating mechanism.

589

You forgot something.

590

And so all of these indices generally

work.

591

pretty well, and that's also what we found

in our study in terms of selecting the

592

correct model when there are some

alternatives that have fewer parameters or

593

picking up on the correct model fitting

well by itself versus models that forget

594

these parameters.

595

But what we were really interested in is

looking at, OK, how well do these indices

596

actually detect overfitting?

597

So that's where you add parameters that

you don't really need.

598

So you're making your model overly

complex.

599

And when we have models that are too

complex, they tend not to generalize to

600

new samples, right?

601

They're optimized for our specific sample

and that's not really useful in science.

602

So we want to make sure that we don't keep

going and like adding paths and making our

603

models super complicated.

604

And so surprisingly what we found across

like a range of over fitting scenarios is

605

that they do not really do a good job of

detecting any of this.

606

Most indices, if anything, just make the

model look better and better and better.

607

Even some of these indices, like model

selection indices, will have a penalty

608

term in their formula that's supposed to

penalize for having too many parameters,

609

right?

610

For making your model too complex.

611

And even those were just like, yeah, this

is fine.

612

Keep going, keep going.

613

And so that's a little bit worrisome.

614

And I think...

615

We really need to think about developing

some new ways of detecting when we go too

616

far, right?

617

Figuring out at what point we need to stop

in our model modification, which is

618

something that researchers really love to

do, especially in structural equation

619

modeling.

620

I won't speak for any other areas.

621

And so, yeah, I think there's a lot of

work to be done.

622

And I was very surprised that these

indices that are supposed to help us

623

detect overfitting also didn't really do.

624

a good job.

625

And so I'm excited to work more on this.

626

I would say in general, if people want an

actionable takeaway, it is always helpful

627

when you have multiple models to compare

versus just your one model of interest.

628

It will help you tease, sort of figure out

better, which one is the correct one

629

versus just is your model good enough?

630

And so that would be my, my advice for

researchers.

631

Yeah.

632

Yeah, definitely.

633

I always like having a very basic and dumb

looking linear regression to compare to

634

that and build my way on top of that

because you can already do really cool

635

stuff with plain simple linear regression

and why making it harder if you cannot

636

prove, you cannot discern a particular

effect of...

637

of the new method you're applying.

638

Yeah.

639

And so do you have then from from your

dive into these, do you have some fit

640

indices that you recommend?

641

And how do they compare to traditional fit

indices?

642

So I think for model

643

fit of a single model within structural

equation modeling.

644

The most popular ones are called

comparative fit index, the Tucker Lewis

645

index, and then the root mean square error

of approximation.

646

You'll see these in like every single

paper published.

647

And so there are Bayesian versions of

those indices, but based on all my

648

research using those so far,

649

I would actually not recommend those at

all for evaluating the fit of your

650

specific model.

651

It seems from at least my research that

they are very sensitive to your sample

652

size, which means that as you get a larger

and larger sample, your model will just

653

keep looking better and better and better

and better, even if it's wrong.

654

So something that would be flagged as like

a...

655

a misspecified model with a small sample

might look perfectly fine with a large

656

sample.

657

And so that's not what you want, right?

658

You want the fit index to reflect the

misspecification, not your sample size.

659

And so I was really excited when these

were first introduced, but I think we need

660

a lot more knowledge about how to actually

use them before they are really useful.

661

And so my advice for researchers who want

to know something about their fit is

662

really to look at

663

the posterior predictive checks.

664

And within structural equation modeling,

I'm not sure how widespread this is for

665

other methods, but we have something

called a posterior predictive p -value,

666

where we basically take our observed data

and evaluate the fit of that data to our

667

model at each posterior iteration.

668

For example, using a likelihood ratio test

or like a chi -square or something.

669

And then we do the same for a posterior

predicted sample.

670

using this in within each of those samples

as well.

671

And the idea is that if your model fits

your data well, then about half of the

672

predictive samples should fit better and

the other half should fit worse, right?

673

Yours should be nicely cozy in the middle.

674

If all of your posterior predictive

samples fit worse than your actual data,

675

then it's an indication that you are

overfitting, right?

676

Like,

677

the model will never fit as well as it

does for your specific data.

678

And so I think in that sense, that index

could potentially give some idea of

679

overfitting, although again, in our study,

we didn't really see that happening.

680

But I think it's a more informative method

of looking at fit within Bayesian

681

structural equation modeling.

682

And so even though it's kind of old

school, I think it's still probably the...

683

the best option for researchers to look

at.

684

Okay, yeah, thanks.

685

That's like, I love that.

686

That's very practical.

687

And I think listeners really appreciate

that.

688

I have like, I was wondering about SEMs

again, and if you have an example from

689

your research where Bayesian SEM provided

significant insights that

690

traditional methods might have missed.

691

Yeah, so some work I'm working on right

now is with a group of researchers who are

692

really interested in figuring out how

strong the evidence is that there is no

693

effect, right?

694

That some path is zero within a bigger

structural model.

695

And with frequentist analysis, all we can

really do is fail to reject the known,

696

right?

697

We have an absence of evidence.

698

but that doesn't mean that there's

evidence of absence.

699

And so we can't really quantify like how

strong or how convinced we should be that

700

that null is really a null effect.

701

But with Bayesian methods, we have base

factors, right?

702

And we can actually explicitly test the

evidence in favor of the estimate being

703

zero versus the estimate being not zero,

right?

704

Either smaller or larger than zero.

705

And so that's really...

706

When I talked to the applied researchers,

once they came to me with this problem,

707

which started as just like a structural

equation modeling problem, but then I was

708

like, well, have you ever considered using

Bayesian methods?

709

Because I feel like it could really help

you get at that question.

710

Like how strong is that evidence relative

to the evidence for an effect, right?

711

And so we've been working on that right

now and it is very interesting to see the

712

results and then also to communicate that

with them and see.

713

They get so excited about it.

714

So that's been fun.

715

Yeah, for sure.

716

That's super cool.

717

And you don't have anything to share in

the show notes yet, right?

718

Not yet.

719

No, I'll keep you posted.

720

Yeah, for sure.

721

Because maybe by the time of publication,

you'll have something for us.

722

Yes.

723

And now I'd like to talk a bit about

your...

724

your teaching because you teach a lot of

classes.

725

You've talked a bit about that already at

the beginning of the show, but how do you

726

approach teaching Bayesian methods to

students in your program, which is the

727

statistics measurement and evaluation and

indication program?

728

Yeah, so I got to be honest and say I have

never taught an entire class on Bayesian

729

methods yet.

730

I'm very excited that I just talked with

my colleagues and I got the okay to

731

develop it and put it on the schedule.

732

So it's coming.

733

But I did recently join a panel

discussion, which was about teaching

734

Bayesian methods.

735

It was organized by the Bayesian Education

Research and Practice Section of the ISBA

736

Association.

737

And so the other two panelists, I was

really starstruck.

738

to be honest, were E .J.

739

Wagemakers and Joachim van de Kerkoven,

which are like, to me, those are really

740

big names.

741

And so talking to them, I really learned a

lot during that panel.

742

I felt like I was more on the panel as a

as an audience member, but it was great

743

for me.

744

And and so from that, I think if I do get

to teach a class on Bayesian methods,

745

which hopefully will be soon.

746

I think I really want to focus on showing

students the entire Bayesian workflow,

747

right?

748

Just as we were talking about, starting

with figuring out priors, prior predictive

749

checks, maybe some of that fancy

calibration.

750

And then also doing sensitivity analyses,

looking at the fit with the posterior

751

predictive samples, all of that stuff.

752

I think...

753

For me, I wouldn't necessarily combine

that with structural equation models

754

because those are already pretty

complicated models.

755

And so I think within a class that's

really focused on Bayesian methods, I

756

would probably stick to a simple but

general model, such as a linear regression

757

model, for example, to illustrate all of

those steps.

758

Yeah, I've been just buying, like I have a

whole bookshelf now of books on Bayesian

759

and teaching Bayesian.

760

And so I'm excited to start reading those.

761

developing my class soon yeah that's super

exciting well done congrats on that i'm

762

glad to hear that so first eg vagon makers

was on the show i don't remember which

763

episode but i will definitely link to it

in the show notes and second yeah which

764

books are you are you gonna use well

765

Good question.

766

So there's one that I kind of like, but it

is very broad, which is written by David

767

Kaplan, who's at the University of

Wisconsin Madison.

768

And it's called, I think, vision

statistics for the social sciences.

769

And so what I like about it is that many

of the examples that are used throughout

770

the book are very relevant to the students

that I would be teaching.

771

And it also covers a wide range.

772

of models, which would be nice.

773

But now that I've like philosophically

switched more to this workflow

774

perspective, it's actually a little bit

difficult to find a textbook that covers

775

all of those.

776

And so I may have to rely a lot on some of

the online resources.

777

I know there's some really great posts by,

I'm so bad with names.

778

I want to say his name is Michael

something.

779

Where he talks about workflow.

780

Yes, probably.

781

Yes, that sounds familiar.

782

His posts are really informative and so I

would probably rely on those a lot as

783

well.

784

Especially because they also use

relatively simpler models.

785

I think, yeah, for some of the components

of the workflow that they just haven't

786

been covered in textbooks as much yet.

787

So if anyone is writing a book right now,

please add some chapters on those lesser

788

known.

789

components, that would be great.

790

Yeah.

791

Yeah, so there is definitely Michael

Bedoncourt's blog.

792

And I know Andrew Gelman is writing a book

right now about the Bayesian workflow.

793

So the Bayesian workflow paper.

794

Yeah, that's a good paper.

795

Yeah, I'll put it in the show notes.

796

But basically, he's turning that into a

book right now.

797

Amazing.

798

Yeah, so it's gonna be perfect for you.

799

And have you taken a look at his latest

book, Active Statistics?

800

Because that's exactly for preparing

teachers to teach patient stats.

801

Yes, he has like an I feel like an older

book as well where he has these

802

activities, but it's really nice that he

came out with this newer, more recent one.

803

I haven't read it yet, but it's on my

804

on my to buy list.

805

I have to buy these books through the

department, so it takes a while.

806

Yeah, well, and you can already listen to

episode 106 if you want.

807

He was on the show and talked exactly

about these books.

808

Amazing.

809

I'll put it in the show notes.

810

And what did we talk about?

811

There was also Michael Betancourt, E .G.

812

Wagenmarkers,

813

Active statistics, microbed and code,

yeah, and the Bayesian workflow paper.

814

Yeah, thanks for reminding me about that

paper.

815

Yeah, it's a really good one.

816

I think it's going to be helpful.

817

I'm not sure they cover SBC already, but

that's possible.

818

But SBC, in any case, you'll have it in

the Bayes flow tutorial that I already

819

linked to in the show notes.

820

So I'll put out that.

821

And actually, what are future developments

in Bayesian stats that excite you the

822

most, especially in the context of

educational research?

823

Well, what you just talked about, and this

amortized estimation thing is very

824

exciting to me.

825

I think, as I mentioned, one of the

biggest hurdles for people switching to

826

Bayesian methods is just the time

commitment, especially with structural

827

equation models.

828

And so knowing that people are working on

algorithms that will speed that up, even

829

for a single analysis, it's just really

exciting to me.

830

And in addition to that, sort of in a

similar vein, I think a lot of smart

831

people are working on software, which is

lowering barriers to entry.

832

People in education, they know a lot about

education, right?

833

That's their field, but they don't have

time to really dive into.

834

Bayesian statistics.

835

And so for a long time, it was very

inaccessible.

836

But now, for example, as you already

mentioned, Ed Merkel, he has his package

837

Blavan, which is great for people who are

interested in structural equation modeling

838

and Bayesian methods.

839

And sort of similarly, you have that

Berkner has that BRMS package.

840

And then if you want to go even more

accessible, there's JASP.

841

which is that point and click sort of

alternative to SPSS, which I really enjoy

842

showing people to let them know that they

don't need to be afraid that they'll lose

843

access to SPSS at some point in their

life.

844

So I think those are all great things.

845

And in a similar vein, there are so many

more online resources now.

846

Then when I first started learning about

base, like when people have questions or

847

they want to get started, I have so many

links to send them of like papers, online

848

courses, YouTube videos, podcasts like

this one.

849

and so I think that's, what's really

exciting to me, not so much what we're

850

doing behind the scenes, right?

851

The actual method itself, although that's

also very exciting, but for working with

852

people.

853

in education or other applied fields.

854

I'm glad that we are all working on making

it easier.

855

So, yeah.

856

Yeah.

857

So first, thanks a lot for recommending

the show to people.

858

I appreciate it.

859

And yeah, completely resonate with what

you just told.

860

Happy to hear that the educational efforts

are.

861

useful for sure that's something that's

very dear to my heart and I spend a lot of

862

time doing that so my people and yeah as

you are saying it's already hard enough to

863

know a lot about educational research but

if you have to learn a whole new

864

statistical framework from scratch it's

very hard and more than that it's not

865

really valued and incentivized in the

academic realm so like why would you even

866

spend time doing that?

867

you'd much rather write a paper.

868

So that's like, that's for sure that's an

issue.

869

So yeah, definitely working together on

that is definitely helping.

870

And on that note, I put all the links in

the show notes and also Paul Burkner was

871

on the show episode 35.

872

So for people who want to dig deeper about

Paul's work, especially BRMS, as you

873

mentioned Sonia.

874

definitely take a well give a give a

listen to that to that episode and also

875

for people who are using Python more than

are but really like the formula syntax

876

that BRMS has you can do that in Python

you can use a package called BAMI and it's

877

basically BRMS in in Python in the

878

that's built on top of PimC and that's

built by a lot of very smart and cool

879

people like my friend Tomica Pretto.

880

He's one of the main core developers.

881

I just released actually an online course

with him about advanced regression in

882

Bambi and Python.

883

So it was a fun course.

884

We've been developing that for the last

two years and we released that this week.

885

So I have to say I'm quite relieved.

886

Congratulations.

887

Yeah, that's exciting.

888

Yeah, that was a very fun one.

889

It's just, I mean, it took so much time

that because we wanted something that was

890

really comprehensive and as evergreen as

it gets.

891

So we didn't want to do something, you

know, quick and then having to do it all

892

over again one year later.

893

So I wanted to take our time and basically

take people from normal linear regression

894

and then okay, how do you generalize that?

895

How do you handle?

896

non -normal likelihoods, how do you handle

several categories?

897

Because most of the examples out there in

the internet are somewhat introductory.

898

How do you do Poisson regression and

binomial regression most of the time?

899

But what about the most complex cases?

900

What happens if you have zero inflated

data?

901

What happens if you have data that's very

dispersed that a binomial or a Poisson

902

cannot handle?

903

What happens if you have multi -category

called data?

904

More than two categories.

905

You cannot use the binomial.

906

You have to use the category called all

the multinomial distributions.

907

And these ones are harder to handle.

908

You need another link function that the

inverse logit.

909

So it's a lot of stuff.

910

But the cool thing is that then you can do

really powerful models.

911

And if you marry that with hierarchical

models, that is really powerful stuff that

912

you can do.

913

So yeah, that's what the whole course is

about.

914

I'll have Tommy actually on the show to

talk about that with him.

915

So that's going to be a fun one.

916

Yeah, I'm looking forward to hearing more

about it.

917

Sounds like something I might recommend to

some people that I know.

918

Yeah, yeah.

919

that's exciting.

920

Yeah, yeah, for sure.

921

Happy to.

922

Happy to.

923

like send you send you the link I put the

link in the show notes anyway so that

924

people who are interested can can take a

look and of course patrons of the show

925

have a 10 % discount because they are they

are the best listeners in the world so you

926

know they deserve a gift yes they are well

Sonya I've already taken quite a lot of

927

your time so we're gonna we're gonna start

closing up but

928

I'm wondering if you have any advice to

give to aspiring researchers who are

929

interested in incorporating Bayesian

methods into their own work and who are

930

working in your field, so educational

research?

931

Yeah, I think the first thing I would say

is don't be scared, which I say a lot when

932

I talk about statistics.

933

Don't be scared and take your time.

934

I think...

935

A lot of people may come into Bayesian

methods after hearing about frequentist

936

methods for years and years and years.

937

And so it's going to take more than a week

or two to learn everything you need to

938

know about Bayes, right?

939

That's normal.

940

We don't expect to familiarize ourselves

with a whole new field in a day or a week.

941

And that's fine.

942

Don't feel like a failure.

943

Then.

944

I don't know, I would also try and look

for papers in your field, right?

945

Like if you're studying school climate, go

online and search for school climate base

946

and see if anyone else has done any work

on your topic of interest using this new

947

estimation method.

948

It's always great to see examples of how

other people are using it within a context

949

that you are familiar with, right?

950

You don't have to start reading all these

technical papers.

951

You can stay within your realm of

knowledge, within your realm of expertise,

952

and then just eke out a little bit.

953

And then after that, I mean, as we just

talked about, there are so many resources

954

available that you can look for, and a lot

of them are starting to become super

955

specific as well.

956

So if you are interested in structural

equation models, go look for resources

957

about Bayesian structural equation

modeling.

958

But if you're interested in some other

model, try and find resources specific to

959

those.

960

And as you're going through this process,

a nice little side benefit that's going to

961

happen is that you're going to get really

good at Googling because you've got to

962

find all this information.

963

But it's out there and it's there to find.

964

So, yeah, that would really be my advice.

965

Don't be scared.

966

Yeah, it's a good one.

967

That's definitely a good one because

then...

968

Like if you're not scared to be

embarrassed or fail, you're gonna ask a

969

lot of questions, you're gonna meet

interesting people, you're gonna learn way

970

faster than you thought.

971

So yeah, definitely great advice.

972

Thanks, Sonja.

973

And people in our field, Invasion Methods,

they are so nice.

974

I feel like they are just so excited

when...

975

I'm so excited when anyone shows any

interest in what I do.

976

Yeah, don't be scared to reach out to

people either because they're going to be

977

really happy that you did.

978

True, true.

979

Yeah, very good point.

980

Yeah, I find that community is extremely

welcoming, extremely ready to help.

981

And honestly, I still have to find trolls

in that community.

982

That's really super value.

983

I feel like it helps that a lot of us came

into this area through also kind of like a

984

roundabout way, right?

985

I don't think anyone is born thinking

they're going to be a Beijing statistician

986

and so we understand.

987

Yeah, yeah.

988

Yeah, well, I did.

989

I think my first word was prior.

990

So, you know, okay.

991

Well, you're the exception to the rule.

992

Yeah, yeah.

993

But you know, that's life.

994

I'm used to being the black sheep.

995

That's fine.

996

no, I think I wanted to be a football

player or something like that.

997

no, also I wanted to fly planes.

998

I wanted to be a fighter pilot at some

point later after I had outgrown football.

999

You're a thrill seeker.

Speaker:

I wanted to be a vet or something, but

then I had to take my pets to the vet and

Speaker:

they were

Speaker:

bleeding and I was like, no, I don't want

the event anymore.

Speaker:

Well, it depends on the kind of animals

you treat, but veterinarian can be a

Speaker:

thrill seeking experience too.

Speaker:

You know, like if you're specialized in

snakes or grizzlies or lions, I'm guessing

Speaker:

it's not all the time, you know, super,

super easy and tranquil.

Speaker:

no.

Speaker:

Awesome.

Speaker:

Well Sonia, that was really great to have

you on the show.

Speaker:

Of course, I'm going to ask you the last

two questions.

Speaker:

Ask every guest at the end of the show.

Speaker:

So if you had unlimited time and

resources, which problem would you try to

Speaker:

solve?

Speaker:

I thought about this a lot because I

wanted to solve many problems.

Speaker:

So when I give this answer, I'm hoping

that other people are taking care of all

Speaker:

those other problems.

Speaker:

But I think something that I've noticed

recently is that a lot of people seem to

Speaker:

have lost the ability or the interest in

critical thinking and being curious and

Speaker:

trying to figure out things by yourself.

Speaker:

And so that's something that I would like

to.

Speaker:

solve or improve somehow?

Speaker:

Don't ask me how, but I think being a

critical thinker and being curious are two

Speaker:

really important skills to have to succeed

in our society right now.

Speaker:

I mean, there's so much information being

thrown at us that it's really up to you to

Speaker:

figure out what to focus on and what to

ignore.

Speaker:

And for that, you really need this

critical thinking skill and...

Speaker:

and also the curiosity to actually look

for information.

Speaker:

And so I think that's, it's also a very

educational problem, I feel.

Speaker:

So if it's where I am right now in my

career, but yeah, that would be something

Speaker:

to solve.

Speaker:

Yeah.

Speaker:

Completely understand that was actually my

answer also.

Speaker:

So I like, really?

Speaker:

Yeah.

Speaker:

Yeah.

Speaker:

Yeah.

Speaker:

I completely agree with you.

Speaker:

Yeah.

Speaker:

These are topics I found.

Speaker:

I find them.

Speaker:

I find super interesting.

Speaker:

How do you.

Speaker:

do we teach critical thinking, how do we

teach the scientific methods, things like

Speaker:

that.

Speaker:

It's always something I'm super excited to

talk about.

Speaker:

Yeah, I also hope it will have some sort

of trickle down effect on all the other

Speaker:

problems, right?

Speaker:

Once the whole world is very skilled at

critical thinking, all the other issues

Speaker:

will be resolved pretty quickly.

Speaker:

Yeah, not only because it's directly

solved, but...

Speaker:

I would say mainly because then you have

maybe less barriers.

Speaker:

And so yeah, probably coming from that.

Speaker:

And then second question, if you could

have dinner with any great scientific

Speaker:

mind, dead, alive or fictional food.

Speaker:

So I ended up

Speaker:

Choosing Ada Lovelace who's like one of

the first or maybe the first woman who

Speaker:

ever worked in computer programming area.

Speaker:

I think she's very interesting I also

recently found out that she passed away

Speaker:

when she was only like 36 Which is like

I'm I'm getting at that age and she

Speaker:

already accomplished all these things By

the time she passed away and so now I'm

Speaker:

like, okay I gotta I gotta step it up, but

I would really love to talk to her about

Speaker:

just her experience.

Speaker:

being so unique in that very manly world

and in that very manly time in general, I

Speaker:

think it would be very interesting to hear

the challenges and also maybe some

Speaker:

advantages or like benefits that she saw,

like why did she go through all this

Speaker:

trouble to begin with?

Speaker:

Yeah, I think it would be an interesting

conversation to have for sure.

Speaker:

Yeah, yeah, definitely.

Speaker:

Yeah, great choice.

Speaker:

I think, I think somebody already

Speaker:

had answered.

Speaker:

I don't remember who, but yeah, it's not a

very common choice.

Speaker:

We can have a dinner party together.

Speaker:

Yeah, exactly.

Speaker:

That's perfect.

Speaker:

Fantastic.

Speaker:

Great.

Speaker:

Thank you.

Speaker:

Thank you so much, Sonja.

Speaker:

That was a blast.

Speaker:

I learned so much.

Speaker:

Me too.

Speaker:

You're welcome.

Speaker:

And well, as usual, I put resources and a

link to a website.

Speaker:

in the show notes for those who want to

dig deeper.

Speaker:

Thank you again, Sonia, for taking the

time and being on this show.

Speaker:

Yeah, thank you.

Speaker:

It was so much fun.

Speaker:

This has been another episode of Learning

Bayesian Statistics.

Speaker:

Be sure to rate, review, and follow the

show on your favorite podcatcher, and

Speaker:

visit learnbaystats .com for more

resources about today's topics, as well as

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access to more episodes to help you reach

true Bayesian state of mind.

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That's learnbaystats .com.

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Check out his awesome work at bababrinkman

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.com slash LearnBasedDance.

Speaker:

Thank you so much for listening and for

your support.

Speaker:

You're truly a good Bayesian.

Speaker:

Change your predictions after taking

information in.

Speaker:

And if you're thinking I'll be less than

amazing, let's adjust those expectations.

Speaker:

Let me show you how to be a good Bayesian

Change calculations after taking fresh

Speaker:

data in Those predictions that your brain

is making Let's get them on a solid

Speaker:

foundation