When Osvaldo asked me to write the foreword to his new book I felt honored, excited, and a bit scared, so naturally I accepted. What follows is my best attempt to convey what makes probabilistic programming so exciting to me. Osvaldo did a great job with the book, it is …

# Using Bayesian Decision Making to Optimize Supply Chains

(c) 2019 Thomas Wiecki & Ravin Kumar

As advocates of Bayesian statistics in data science we often have to convince business-minded colleagues or customers of the added value of such an approach. While there are many good reasons for applying Bayesian modeling to solve business problems (Sean J Taylor recently had …

# Hierarchical Bayesian Neural Networks with Informative Priors

(c) 2018 by Thomas Wiecki

Imagine you have a machine learning (ML) problem but only small data (*gasp*, yes, this does exist). This often happens when your data set is nested -- you might have many data points, but only few per category. For example, in ad-tech you may want predict …

# An intuitive, visual guide to copulas

(c) 2018 by Thomas Wiecki

People seemed to enjoy my intuitive and visual explanation of Markov chain Monte Carlo so I thought it would be fun to do another one, this time focused on copulas.

If you ask a statistician what a copula is they might say "a copula is …

# What's new in PyMC3 3.1

We recently released PyMC3 3.1 after the first stable 3.0 release in January 2017. You can update either via `pip install pymc3`

or via `conda install -c conda-forge pymc3`

.

A lot is happening in PyMC3-land. One thing I am particularily proud of is the developer community we have …