The Bayesian Zig Zag: Developing and Testing PyMC Models (Allen Downey)

Speaker: Allen Downey
Title: The Bayesian Zig Zag: Developing and Testing PyMC Models
Video: • The Bayesian Zig Zag: ...
Event description:
Tools like PyMC make it easy to implement probabilistic models, but it is still challenging to develop and validate those models. In this talk, I present an incremental strategy for developing and testing models by alternating between forward and inverse probabilities and between grid algorithms and MCMC. I’ll use Poisson processes as an example, but this strategy applies to other probabilistic models.
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Speaker bio:
Allen Downey is a professor of Computer Science at Olin College and Visiting Lecturer at Ashesi University in Ghana. He is the author of a series of open-source textbooks related to software and data science, including Think Python, Think Bayes, and Think Complexity, which are also published by O’Reilly Media. His blog, Probably Overthinking It, features articles on Bayesian probability and statistics. He holds a Ph.D. in computer science from U.C. Berkeley, and M.S. and B.S. degrees from MIT.
Speaker info:
-Twitter: / allendowney
-GitHub: github.com/All...
-Website: allendowney.com/
Part of PyMCon2020.
More details at www.pymcon.com
#bayesian #PyMC #probabilistic

[@matthewgraziano1124]

Excellent talk. I appreciated the commitment to starting simple and iteratively adding complexity. Many resources dive right into complexity without robustly detailing the “why”

[@DataTranslator]

This didactically extraordinary 🎉