With thousands of parameters with little specialized knowledge ofįor fast approximate posterior estimation as well as mini-batch ADVIĬomputation optimization and dynamic C or JAX compilation Powerful sampling algorithms, such as the No U-Turn Intuitive model specification syntax, for example, x ~ N(0,1) Or rather its successors Theano-PyMC ( pymc3 =4).Ĭheck out `_) and specifically the latest developments on the PyMC3 `main branch `. Since then many things changed and we are happy to announce that PyMC3 will continue to rely on Theano, Stopped getting developed by the original authors, and we started experiments with a PyMC version based on tensorflow probability. There have been many questions and uncertainty around the future of PyMC3 since Theano Its flexibility and extensibility make it applicable to aįor questions on PyMC3, head on over to our PyMC Discourse forum. PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learningįocusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI)Īlgorithms.
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