Ep. 48: PyMC

Featuring Developer: Thomas Wiecki

Episode #48

Air Date 27 August 2020

@11 AM Eastern

We will be rejoined by Thomas Wiecki who will talk about the work being done with PyMC. 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 Theano which provides:

Computation optimization and dynamic C compilation

Numpy broadcasting and advanced indexing

Linear algebra operators

Simple extensibility

Transparent support for missing value imputation