Bayesian Modeling and Probabilistic Machine Learning with Aesara

Jul 09, 2021 1 min read

PyMC3

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Its flexibility and extensibility make it applicable to a large suite of problems.

Features

Intuitive model specification syntax, for example, x ~ N(0,1) translates to x = Normal('x',0,1)
Powerful sampling algorithms, such as the No U-Turn Sampler, allow complex models with thousands of parameters with little specialized knowledge of fitting algorithms.
Variational inference: ADVI for fast approximate posterior estimation as well as mini-batch ADVI for large data sets.
Relies on Aesara which provides:
- Computation optimization and dynamic C or JAX compilation
- Numpy broadcasting and advanced indexing
- Linear algebra operators
- Simple extensibility
Transparent support for missing value imputation

GitHub

https://github.com/pymc-devs/pymc3

Machine Learning Data Analysis

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