This Friday afternoon I (Jonah) will be speaking about Stan at Washington University in St. Louis. The talk is open to the public, so anyone in the St. Louis area who is interested in Stan is welcome to attend. Here are the details: Title: Stan: A Software Ecosystem for Modern Bayesian Inference Jonah Sol Gabry, […]

**Stan**

## Stan Conference Live Stream

StanCon 2017 is tomorrow! Late registration ends in an hour. After that, all tickets are $400. We’re going to be live streaming the conference. You’ll find the stream as a YouTube Live event from 8:45 am to 6 pm ET (and whatever gets up will be recorded by default). We’re streaming it ourselves, so if there are […]

## StanCon: now accepting registrations and submissions

As we announced here a few weeks ago, the first Stan conference will be Saturday, January 21, 2017 at Columbia University in New York. We are now accepting both conference registrations and submissions. Full details are available at StanCon page on the Stan website. If you have any questions please let us know and we […]

## ShinyStan v2.0.0

For those of you not familiar with ShinyStan, it is a graphical user interface for exploring Stan models (and more generally MCMC output from any software). For context, here’s the post on this blog first introducing ShinyStan (formerly shinyStan) from earlier this year. ShinyStan v2.0.0 released ShinyStan v2.0.0 is now available on CRAN. This is […]

## A Stan is Born

Stan 1.0.0 and RStan 1.0.0 It’s official. The Stan Development Team is happy to announce the first stable versions of Stan and RStan. What is (R)Stan? Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It’s sort of like BUGS, but with a different language […]

## Learning Differential Geometry for Hamiltonian Monte Carlo

You can get a taste of Hamiltonian Monte Carlo (HMC) by reading the very gentle introduction in David MacKay’s general text on information theory: MacKay, D. 2003. Information Theory, Inference, and Learning Algorithms. Cambridge University Press. [see Chapter 31, which is relatively standalone and can be downloaded separately.] Follow this up with Radford Neal’s much […]