Automatic MCMC hyperparameter sensitivity measurements in Stan -- A worked example

Ryan Giordano / January 31, 2018

We will now follow up our previous post on automatic MCMC hyperparameter sensitivity with a detailed use case taken from the Stan examples. All the code to produce the results below, as well as more examples, can be found in the examples folder on the rgiordan/StanSensitivity git repo. This simple, real-life example has a number of interesting features:

Automatic MCMC hyperparameter sensitivity measurements in Stan

Ryan Giordano / January 19, 2018

A Bayesian approach to statistical modeling comes with many advantages. For example, it's the only logically coherent way to model uncertainty of parameter estimates! Being Bayesian has never been easier than it is now, thanks to high-quality, easy-to-use automatic tools like Stan.

Beauty vs. Function: Not a Problem in Superheat

/ March 20, 2017

by Kasia Metkowski Data meets narrative in Rebecca Barter’s Superheat, an R package that creates colorful and customizable heatmaps. 

The State of Jupyter

Fernando Perez / February 14, 2017

This post was originally published at the O'Reilly Ideas site on January 26, 2017. In this post, we’ll look at Project Jupyter and answer three questions:

Simple Random Sampling: Not So Simple

Kellie Ottoboni / February 3, 2017

Simple random sampling is drawing k objects from a group of n in such a way that all possible subsets are equally likely. In practice, it is difficult to draw truly random samples. Instead, people tend to draw samples using