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.
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.
This is a PyMCon 2020 talk
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