You wanna know something funny? A sentence from this episode became a meme. And people even made stickers out of it! Ok, that’s not true. But if someone could pull off something like that, it would surely be Chelsea Parlett-Pelleriti.
Indeed, Chelsea’s research focuses on using statistics and machine learning on behavioral data, but her more general goal is to empower people to be able to do their own statistical analyses, through consulting, education, and, as you may have seen, stats memes on Twitter.
A full-time teacher, researcher and statistical consultant, Chelsea earned an MsC and PhD in Computational and Data Science in 2021 from Chapman University. Her courses include R, intro to programming (in Python), and data science.
In a nutshell, Chelsea is, by her own admission, an avid lover of anything silly or statistical. Hopefully, this episode turned out to be both at once! I’ll let you be the judge of that…
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) !
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin and Philippe Labonde.
Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)
Links from the show:
Chelsea's website: https://cmparlettpelleriti.github.io/index.html (https://cmparlettpelleriti.github.io/index.html)
Chelsea on Twitter: https://twitter.com/ChelseaParlett (https://twitter.com/ChelseaParlett)
Michael Betancourt's sparsity case study: https://betanalpha.github.io/assets/case_studies/modeling_sparsity.html (https://betanalpha.github.io/assets/case_studies/modeling_sparsity.html)
LBS #31 -- Bayesian Cognitive Modeling & Decision-Making, with Michael Lee: https://www.learnbayesstats.com/episode/31-bayesian-cognitive-modeling-michael-lee (https://www.learnbayesstats.com/episode/31-bayesian-cognitive-modeling-michael-lee)
Projection predictive variable selection R package: https://mc-stan.org/projpred/
SelectiveInference R package: https://cran.r-project.org/web/packages/selectiveInference/selectiveInference.pdf (https://cran.r-project.org/web/packages/selectiveInference/selectiveInference.pdf)
Statistical learning and selective inference: https://www.pnas.org/content/112/25/7629 (https://www.pnas.org/content/112/25/7629)
LBS #29 -- Model Assessment, Non-Parametric Models, with Aki Vehtari: https://www.learnbayesstats.com/episode/model-assessment-non-parametric-models-aki-vehtari (https://www.learnbayesstats.com/episode/model-assessment-non-parametric-models-aki-vehtari)
LBS #35 -- The Past, Present & Future of BRMS, with Paul Bürkner: https://www.learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner (https://www.learnbayesstats.com/episode/35-past-present-future-brms-paul-burkner)
BRMS R Package: https://paul-buerkner.github.io/brms/ (https://paul-buerkner.github.io/brms/)
Bayesian Item Response Modeling in R with BRMS and Stan: https://arxiv.org/pdf/1905.09501.pdf (https://arxiv.org/pdf/1905.09501.pdf)
BAyesian Model-Building Interface (Bambi)...