Bayesian Models

Introducing Stan2tfp - a lightweight interface for the Stan-to-TensorFlow Probability compiler

TL;DR The new Stan compiler has an alternative backend that allows you to do this: stan2tfp is a lightwight interface for this compiler, that allows you to do this with one line of code, and fit the model to data with another. Why stan2tfp In short - to get the convenience of Stan programs and the scalability of TensorFlow. The model is written in Stan, which means you get a lot of the benefits of having the Stan compiler behind your shoulder (types, bounds, etc).

Mr. P meets TFP - mixed effects model with post-stratification in TensorFlow Probability

TL;DR We’ll: Learn an interesting method for generalizing inferences from a biased sample to a population of interest See why prior predictive checks are great Implement a simple mixed-effects model in TFP Intro This post is a TFP port of Lauren Kennedy and Jonah Gabry’s excellent MRP with rstanarm vignette. It describes a very interesting statistical method for generalizing inferences from a biased sample to a population of interest.

Bayesian golf puttings, NUTS, and optimizing your sampling function with TensorFlow Probability

TL;DR We’ll: Port a great Bayesian modelling tutorial from Stan to TFP Discuss how to speed up our sampling function Use the trace_fn to produce Stan-like generated quantities Explore the results using the ArviZ library. Intro This is a TFP-port one of of the best Bayesian modelling tutorials I’ve seen online - the Model building and expansion for golf putting Stan tutorial. It’s a beautiful example of modeling from first principles, and why the incorporation of domain knowledge into a statistical model - in this case, knowing a little bit about golf and some high-school physics - is so important.