Junpeng Lao: A Hitchhiker's Guide to designing a Bayesian library in Python | PyData Córdoba

Описание: With modern automatic differentiation libraries like Tensorflow, Jax, autograd, Pytorch, Theano, and more (insert your favorite autograd library here), writing a Bayesian library (or aspiringly, a Probabilistic programming language) seems could not be easier. So, what are the challenges? In this talk, I will speak about designing a Bayesian computation library using PyMC3 as an example, and share some stories about our (now) two iteration of designing PyMC4, with some anecdotes on comparing different Bayesian libraries, choosing a new computational backend, TF1 to TF2 transition and graph modification. In a way, this talk is NOT a tutorial of how to design a Bayesian library, but the opposite: I will try to convince you not to write one, unless you want to deal with 3 types of shape issues, find 10 alternative ways to rewriting control flows, and learn a lot of tricks to handle edge cases that could quickly goes obscure.

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