Yee Whye Teh sends along this paper with Leonard Hasenclever, Thibaut Lienart, Sebastian Vollmer, Stefan Webb, Balaji Lakshminarayanan, and Charles Blundell. I haven’t read it in detail but they not similarities to our “expectation propagation as a way of life” paper. But their work is much more advanced than ours.

**Statistical computing**category.

## Stan Coding Corner: O(N) Change-Point Program with Clever Forward-Backward Calculation

It’s so much fun to work in open source. Luke Wiklendt sent along this improved code for a change-point model calculation in Stan. With N data points in the time series, the version in the manual is O(N2), whereas the improved version is O(N). In practice, Luke says [the new code] results in a dramatic […]

## Black Box Challenge

Georgy Cheremovskiy writes: I’m one of the organizers of an unusual reinforcement learning competition named Black Box Challenge. The conception is simple — one need to program an agent that can play a game with unknown rules. At each time step agent is given an environment state vector and has a few possible actions. The […]

## “Rbitrary Standards”

Allen and Michael pointed us on the Stan list to these amusing documents by Oliver Keyes: Rbitrary Standards: “This is an alternate FAQ for R. Specifically, it’s an FAQ that tries to answer all the questions about R’s weird standards, formatting and persnicketiness that you’re afraid to ask.” Parallelism, R, and OpenMP Enjoy.

## Actually, I’d just do full Bayes

Dave Clark writes: I was hoping for your opinion on a topic related to hierarchical models. I am an actuary and have generally worked with the concept of hierarchical models in the context of credibility theory. The text by Bühlmann and Gisler (A Course in Credibility Theory; Springer) sets up the mixed models under the […]

*Stan Case Studies* Launches

There’s a new section of the Stan web site, with case studies meant to illustrate statistical methodologies, classes of models, application areas, statistical computation, and Stan programming. Stan Case Studies The first ten or so are up, including a grab bag of education models from Daniel Furr at U.C. Berkeley: Hierarchical Two-Parameter Logistic Item Response […]

## Bayesian inference for network links

A colleague writes: I’m working with a doctoral student on a latent affinity network problem and we keep hitting challenges in sampling, in our case using Metropolis-Hastings, for the network links. As you can imagine, lots of local modes, things get stuck, etc . . . Any suggestions on how to sample network links? My […]

## TOP SECRET: Newly declassified documents on evaluating models based on predictive accuracy

We recently had an email discussion among the Stan team regarding the use of predictive accuracy in evaluating computing algorithms. I thought this could be of general interest so I’m sharing it here. It started when Bob said he’d been at a meting on probabilistic programming where there was confusion on evaluation. In particular, some […]

## One quick tip for building trust in missing-data imputations?

Peter Liberman writes: I’m working on a paper that, in the absence of a single survey that measured the required combination of variables, analyzes data collected by separate, uncoordinated Knowledge Networks surveys in 2003. My co-author (a social psychologist who commissioned one of the surveys) and I obtained from KN unique id numbers for all […]

## McElreath’s *Statistical Rethinking: A Bayesian Course with Examples in R and Stan *

We’re not even halfway through with January, but the new year’s already rung in a new book with lots of Stan content: Richard McElreath (2016) Statistical Rethinking: A Bayesian Course with Examples in R and Stan. Chapman & Hall/CRC Press. This one got a thumbs up from the Stan team members who’ve read it, and […]

## Stan 2.9 is Here!

We’re happy to announce that Stan 2.9.0 is fully available(1) for CmdStan, RStan, and PyStan — it should also work for Stan.jl (Julia), MatlabStan, and StataStan. As usual, you can find everything you need on the Stan Home Page. The main new features are: R/MATLAB-like slicing of matrices. There’s a new chapter in the user’s […]

## Showdown in Vegas: When the numbers differ in the third decimal place

From the Stan users list: I have just started to look into the output of the optimizing function and it seems to give estimates slightly different than the ones that I had previously obtained through maximum likelihood estimation (using MATLAB). Can you please tell me what is the penatly that the LBFGS algorithm imposes? In […]

## R sucks

I’m doing an analysis and one of the objects I’m working on is a multidimensional array called “attitude.” I took a quick look: > dim(attitude) [1] 30 7 Huh? It’s not supposed to be 30 x 7. Whassup? I search through my scripts for a “attitude” but all I find is the three-dimensional array. Where […]

## Working Stiff

After a challenging development process we are happy to announce that Stan finally supports stiff ODE systems, removing one of the key obstacles in fields such as pharmacometrics and ecology. For the experts, we’ve incorporated CVODE 2.8.2 into Stan and exposed the backward-differentiation formula solver using Newton iterations and a banded Jacobian computed exactly using our autodiff. […]

## My talks at Nips

Today (Fri 11 Dec 2005), 4:30pm, room 514a, The Statistical Crisis in Science, in Workshop on Adaptive Data Analysis Today, 4:55pm, room 513ab, on a panel in Workshop on Advances in Approximate Bayesian Inference Tomorrow (Sat), 9am, room 513ab, Adventures on the Efficient Frontier, in Workshop on Scalable Monte Carlo Also see here.

## Why I decided not to enter the $100,000 global warming time-series challenge

tl;dr: Negative expected return. Long version: I received the following email the other day from Tom Daula: Interesting applied project for your students, or as a warning for decisions under uncertainty / statistical significance. Real money on the line so the length of time and number of entries required to get a winner may be […]