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

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

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.

## Tales from the Docathon: How to Get Communities to Write Documentation

by Chris Holdgraf and Nelle Varoquaux

## Beauty vs. Function: Not a Problem in Superheat

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

## The State of Jupyter

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

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