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A Guide to Plotting Inferences & Uncertainties of Bayesian Models, with Jessica Hullman

A Guide to Plotting Inferences & Uncertainties of Bayesian Models, with Jessica Hullman

Released Friday, 23rd December 2022
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A Guide to Plotting Inferences & Uncertainties of Bayesian Models, with Jessica Hullman

A Guide to Plotting Inferences & Uncertainties of Bayesian Models, with Jessica Hullman

A Guide to Plotting Inferences & Uncertainties of Bayesian Models, with Jessica Hullman

A Guide to Plotting Inferences & Uncertainties of Bayesian Models, with Jessica Hullman

Friday, 23rd December 2022
Good episode? Give it some love!
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Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!

I’m guessing you already tried to communicate the results of a statistical model to non-stats people — it’s hard, right? I’ll be honest: sometimes, I even prefer to take notes during meetings than doing that… But shhh, that’s out secret.

But all of this was before. Before I talked with Jessica Hullman. Jessica is the Ginny Rometty associate professor of computer science at Northwestern University.

Her work revolves around how to design interfaces to help people draw inductive inferences from data. Her research has explored how to best align data-driven interfaces and representations of uncertainty with human reasoning capabilities, which is what we’ll mainly talk about in this episode.

Jessica also tries to understand the role of interactive analysis across different stages of a statistical workflow, and how to evaluate data visualization interfaces.

Her work has been awarded with multiple best paper and honorable mention awards, and she frequently speaks and blogs on topics related to visualization and reasoning about uncertainty — as usual, you’ll find the links in the show notes.

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, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bert≈rand 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, 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, 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, David Haas, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox and Trey Causey.

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

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Learning Bayesian Statistics

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is?Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow.When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped.But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners!My name is Alex Andorra by the way, and I live in Estonia. By day, I'm a data scientist and modeler at the PyMC Labs consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages PyMC and ArviZ. I also love election forecasting and, most importantly, Nutella. But I don't like talking about it – I prefer eating it.So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and unlock exclusive Bayesian swag on Patreon!

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