Do You Have Best Practices to Visualize Uncertainty in the Basin Framework?

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

I have to confess something: I love challenges. And when you’re a podcaster, what’s a better challenge than dedicating an episode to… visualization? Impossible you say? Well, challenge accepted!

Thankfully, I got the help of a visualization Avenger for this episode — namely, Matthew Kay. Matt is an Assistant Professor jointly appointed in Computer Science and Communications Studies at Northwestern University, where he co-directs the Midwest Uncertainty Collective — I know, it’s a pretty cool name for a lab.

He works in human-computer interaction and information visualization, and especially in uncertainty visualization. He also builds tools to support uncertainty visualization in R. In particular, he’s the author of the tidybayes and ggdist R packages, and wrote the random variable interface in the posterior package.

I promise, you won’t be uncertain about the importance of uncertainty visualization after 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/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, 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, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Lin Yu Sha and Scott Anthony Robson.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Matt on Twitter: https://twitter.com/mjskay
• Matt on GitHub: https://github.com/mjskay  
• Matt’s website: https://www.mjskay.com/ 
• Midwest Uncertainty Collective lab: https://mucollective.northwestern.edu/ 
• PyMC find_constrained_priors tutorial: https://www.youtube.com/watch?v=9shZeqKG3M0
• PyMC find_constrained_priors doc: https://www.pymc.io/projects/docs/en/latest/api/generated/pymc.find_constrained_prior.html
• Tutorials / package documentation / videos:
• tidybayes: http://mjskay.github.io/tidybayes/ 
• ggdist: https://mjskay.github.io/ggdist/ (various visualizations in the slabinterval vignette: https://mjskay.github.io/ggdist/articles/slabinterval.html ) 
• Miscellaneous uncertainty visualizations examples: https://github.com/mjskay/uncertainty-examples 
• Talk on uncertainty visualization: https://www.youtube.com/watch?v=E1kSnWvqCw0 
• Biases in probability perception:
• A survey paper on the linear-in-log-odds model of probability perception: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3261445/
• Using the linear-in-log-odds model to "debias" uncertainty visualization: https://osf.io/6xcnw/