Tools like PyMC and Stan make it easy to implement probabilistic models, but getting started can be challenging. In this talk I present a strategy for simultaneously developing and implementing probabilistic models by alternating between forward and inverse probabilities and between grid algorithms and MCMC. This process helps developers validate modeling decisions and verify their implementation. As an example, I will use a version of the "Boston Bruins problem," which I presented in Think Bayes, updated for the 2017-18 season. I will also present and request comments on my plans for the second edition of Think Bayes.
Allen Downey
Professor of Computer Science at Olin College in Needham, Massachusetts, and the author of Think Python, Think Bayes, Think Stats, and several other books related to computer science and data science. Previously he taught at Wellesley College and Colby College, and in 2009 was a Visiting Scientist at Google, Inc. Allen has a Ph.D. from U.C. Berkeley and B.S. and M.S. degrees from MIT. He writes a blog about Bayesian statistics and related topics called Probably Overthinking It. Several of his books are published by O’Reilly Media and all are available under free licenses from Green Tea Press.
