#119 Causal Inference, Fiction Writing and Career Changes, with Robert Kubinec
Learning Bayesian Statistics
Intro
This chapter explores the inspiring transition of an assistant professor from government service to academia, delving into his research on wealth creation and corruption. It also introduces his innovative work, 'The Bayesian Hitman', merging storytelling with statistical principles while outlining the podcast's focus on evidence-based thinking.
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Takeaways:
- Bob's research focuses on corruption and political economy.
- Measuring corruption is challenging due to the unobservable nature of the behavior.
- The challenge of studying corruption lies in obtaining honest data.
- Innovative survey techniques, like randomized response, can help gather sensitive data.
- Non-traditional backgrounds can enhance statistical research perspectives.
- Bayesian methods are particularly useful for estimating latent variables.
- Bayesian methods shine in situations with prior information.
- Expert surveys can help estimate uncertain outcomes effectively.
- Bob's novel, 'The Bayesian Hitman,' explores academia through a fictional lens.
- Writing fiction can enhance academic writing skills and creativity.
- The importance of community in statistics is emphasized, especially in the Stan community.
- Real-time online surveys could revolutionize data collection in social science.
Chapters:
00:00 Introduction to Bayesian Statistics and Bob Kubinec
06:01 Bob's Academic Journey and Research Focus
12:40 Measuring Corruption: Challenges and Methods
18:54 Transition from Government to Academia
26:41 The Influence of Non-Traditional Backgrounds in Statistics
34:51 Bayesian Methods in Political Science Research
42:08 Bayesian Methods in COVID Measurement
51:12 The Journey of Writing a Novel
01:00:24 The Intersection of Fiction and Academia
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, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström and Stefan.
Links from the show:
- Robert’s website (includes blog posts): https://www.robertkubinec.com/
- Robert on GitHub: https://github.com/saudiwin
- Robert on Linkedin: https://www.linkedin.com/in/robert-kubinec-9191a9a/
- Robert on Google Scholar: https://scholar.google.com/citations?user=bhOaXR4AAAAJ&hl=en
- Robert on Twitter: https://x.com/rmkubinec
- Robert on Bluesky: https://bsky.app/profile/rmkubinec.bsky.social
- The Bayesian Hitman: https://www.amazon.com/Bayesian-Hitman-Robert-M-Kubinec/dp/B0D6M4WNRZ/
- Ordbetareg overview: https://www.robertkubinec.com/ordbetareg
- Idealstan – this isn’t out yet, but you can access an older working paper here: https://osf.io/preprints/osf/8j2bt
- Ordinal Regression tutorial, Michael Betancourt: https://betanalpha.github.io/assets/case_studies/ordinal_regression.html
- Andrew Heiss blog: https://www.andrewheiss.com/blog/
Transcript
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