[MODELING WEBINAR] -- The Bayesian Workflow in Biology, with Justin Bois

📢 Here is the recording of our Bayesian Workflow in Biology webinar!
Follow along with the accompanying GitHub repository: github.com/jus...
🔍 The Bayesian approach offers a powerful framework for handling uncertainty in data analysis. But building, evaluating, and using Bayesian models can be challenging and requires statistical skills, subject matter knowledge, and programming expertise.
🔬 This webinar will detail a complete Bayesian statistics workflow -- iterative model building, model checking, computational troubleshooting, model understanding, and model comparison.
🧪 Our guest, Justin Bois, will use the fascinating world of microtubule dynamics and kinesin proteins to emphasize the importance of deliberate decision-making throughout the workflow.
🌟 Microtubules are microscopic, hollow cylindrical structures found within cells. They are composed of tubulin protein subunits and play a critical role in various cellular processes, such as cell division, intracellular transport, and maintaining cell structure.
⚙️ Justin Bois will use this paper to conduct the analysis: linkinghub.els...
🔑 Join us and learn how to conduct an end-to-end Bayesian workflow, biology style!
🎙️ Our guest speaker, Justin Bois, is a Teaching Professor in the Division of Biology and Biological Engineering at Caltech, California, where he also did his PhD. Before that, he was a postdoc in biochemistry at UCLA, as well as the Max Planck Institute in Dresden, Germany.
⚽ Most importantly for the football fans, he’s a goalkeeper. A big fan of the Los Angeles football club, Justin is a also a magic enthusiast - he is indeed a member of the Magic Castle…
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Useful references:
Accompanying paper: www.cell.com/c...
Listen to Justin's story: learnbayesstat...
Follow Learning Bayesian Statistics: learnbayesstat...
Support the show on Patreon: / learnbayesstats
Alex on Twitter: / alex_andorra
Alex on LinkedIn: / aandorra-pollsposition
Justin’s website: bois.caltech.ed...
Justin on GitHub: github.com/jus...
Justin’s course on Bayesian inference: bebi103b.githu...
Street-fighting mathematics: streetfightingm...
Paper with beautiful mitosis movie: doi.org/10.755...

[@niceday2015]

Thank you so much for your wonderful work

[@EdoardoMarcora]

Great talk Justin... remembering the good ol' days at Caltech! :)

[@kyrgyzsanjar]

This is really good! Please keep them coming!

[@learningbayesianstatistics8147]

Thanks, really glad to hear that's useful!

[@sinraall]

Absolutely fantastic. Thanks Justin. Alex, please keep doing these webinar sessions, they add so much value to your excelent podcasts. :-)

[@learningbayesianstatistics8147]

Thanks a lot for your great feedback! Any topic you'd like to hear about in this format?

[@sinraall]

​@@learningbayesianstatistics8147Thanks for asking. I like the idea to be surprised by any guest you invite. :-) Bayesian factor analysis would be great. Or survival models.

[@bradkolb]

This is great stuff.

[@learningbayesianstatistics8147]

Thanks Brad!