Probabilistic programming with PyMC3
Probabilistic programming provides a language to describe and fit probability distributions so that we can design, encode, and automatically estimate and evaluate complex models. It aims to abstract away some of the computational and analytical complexity to allow us to focus on the conceptually more straightforward and intuitive aspects of Bayesian reasoning and inference.
The field has become quite dynamic since new languages emerged after Uber open sourced Pyro (based on PyTorch). Google, more recently, added a probability module to TensorFlow.
As a result, the practical relevance and use of Bayesian methods in ML will likely increase to generate insights into uncertainty and, in particular, for use cases that require transparent rather than black-box models.
In this section, we will introduce the popular PyMC3 library, which implements advanced MCMC sampling and variational inference for ML models using Python. Together with Stan...
