Learning Bayesian Statistics

• Support & get perks!
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• Intro to Bayes and Advanced Regression courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work !

Chapters:
11:37 The Hard Tech Era
21:08 The Shift in Tech Work Culture
28:49 AI's Impact on Job Security and Work Dynamics
34:33 Adapting to AI: Skills for the Future
45:56 Understanding AI Models and Their Limitations
47:25 The Importance of Diversity in AI Development
54:34 Positioning Technical Talent for Job Security
57:58 Building Resilience in Uncertain Times
01:06:33 Recognizing Diverse Ambitions in Career Progression
01:12:51 The Role of Managers in Employee Retention
01:26:55 Solving Complex Problems with AI and Innovation

Thank you to my Patrons for making this episode possible!

Links from the show:
Alana's latest book (Use code BAYESIAN for 10% off + a free interview preparation download PDF)
Alana’s Substack
Alana on Linkedin
Alana on Instagram
The Obstacle Is the Way – The Timeless Art of Turning Trials into Triumph
Courage Is Calling – Fortune Favours the Brave

Today’s clip is from episode 148 of the podcast, with Scott Berry.

In this conversation, Alex and Scott discuss emphasizing the shift from frequentist to Bayesian approaches in clinical trials.

They highlight the limitations of traditional trial designs and the advantages of adaptive and platform trials, particularly in the context of COVID-19 treatment.

The discussion provides insights into the complexities of trial design and the innovative methodologies that are shaping the future of medical research.

Get the full discussion here!

• Join this channel to get access to perks: https://www.patreon.com/c/learnbayesstats

• Intro to Bayes Course (first 2 lessons free): https://topmate.io/alex_andorra/503302
• Advanced Regression Course (first 2 lessons free): https://topmate.io/alex_andorra/1011122

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

• Support & get perks!
• Proudly sponsored by PyMC Labs. Get in touch and tell them you come from LBS!
• Intro to Bayes and Advanced Regression courses (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work !

Chapters:
13:16 Understanding Adaptive and Platform Trials
25:25 Real-World Applications and Innovations in Trials
34:11 Challenges in Implementing Bayesian Adaptive Trials
42:09 The Birth of a Simulation Tool
44:10 The Importance of Simulated Data
48:36 Lessons from High-Stakes Trials
52:53 Navigating Adaptive Trial Designs
56:55 Communicating Complexity to Stakeholders
01:02:29 The Future of Clinical Trials
01:10:24 Skills for the Next Generation of Statisticians

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Giuliano Cruz, 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, 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, Aubrey Clayton, 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, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Joshua Meehl, Javier Sabio, Kristian Higgins, Matt Rosinski, 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, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık, Suyog Chandramouli, Guillaume Berthon, Avenicio Baca, Spencer Boucher, Krzysztof Lechowski, Danimal, Jácint Juhász, Sander and Philippe.

Links from the show:
Berry Consultants
Scott's podcast
LBS #45 Biostats & Clinical Trial Design, with Frank Harrell

Today’s clip is from episode 147 of the podcast, with Martin Ingram.
Alex and Martin discuss the intricacies of variational inference, particularly focusing on the ADVI method and its challenges. They explore the evolution of approximate inference methods, the significance of mean field variational inference, and the innovative linear response technique for covariance estimation.
The discussion also delves into the trade-offs between stochastic and deterministic optimization techniques, providing insights into their implications for Bayesian statistics.
Get the full discussion here.
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Transcript
This is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!
Intro to Bayes Course (first 2 lessons free)
Advanced Regression Course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!
Visit our Patreon page to unlock exclusive Bayesian swag ;)
Takeaways:
DADVI is a new approach to variational inference that aims to improve speed and accuracy.
DADVI allows for faster Bayesian inference without sacrificing model flexibility.
Linear response can help recover covariance estimates from mean estimates.
DADVI performs well in mixed models and hierarchical structures.
Normalizing flows present an interesting avenue for enhancing variational inference.
DADVI can handle large datasets effectively, improving predictive performance.
Future enhancements for DADVI may include GPU support and linear response integration.

Chapters:
13:17 Understanding DADVI: A New Approach
21:54 Mean Field Variational Inference Explained
26:38 Linear Response and Covariance Estimation
31:21 Deterministic vs Stochastic Optimization in DADVI
35:00 Understanding DADVI and Its Optimization Landscape
37:59 Theoretical Insights and Practical Applications of DADVI
42:12 Comparative Performance of DADVI in Real Applications
45:03 Challenges and Effectiveness of DADVI in Various Models
48:51 Exploring Future Directions for Variational Inference
53:04 Final Thoughts and Advice for Practitioners
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Giuliano Cruz, 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, 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, Aubrey Clayton, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël...