How to build probabilistic models with PyMC3 in Bayesian

The basic idea of probabilistic programming with PyMC3 is to specify models using code and then solve them in an automatic way. Probabilistic programming offers an effective way to build and solve complex models and allows us to focus more on model design, evaluation, and interpretation, and less on mathematical or computational details.This post is taken from the book Bayesian Analysis with Python by Packt Publishing written by author Osvaldo Martin. This book discusses PyMC3, a very flexible Python library for probabilistic programming, as well as ArviZ, a new Python library that will help us interpret the results of probabilistic

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