[cat picture] This came up in a recent discussion thread, I can’t remember exactly where. A commenter pointed out, correctly, that you shouldn’t require computer programming as a prerequisite for a statistics course: there’s lots in statistics that can be learned without knowing how to program. Sure, if you can program you can do a […]

**Statistical computing**category.

## I hate R, volume 38942

link R doesn’t allow block comments. You have to comment out each line, or you can encapsulate the block in if(0){} which is the world’s biggest hack. Grrrrr. P.S. Just to clarify: I want block commenting not because I want to add long explanatory blocks of text to annotate my scripts. I want block commenting […]

## Fitting hierarchical GLMs in package *X* is like driving car *Y*

Given that Andrew started the Gremlin theme, I thought it would only be fitting to link to the following amusing blog post: Chris Brown: Choosing R packages for mixed effects modelling based on the car you drive (on the seascape models blog) It’s exactly what it says on the tin. I won’t spoil the punchline, […]

## Bayesian Posteriors are Calibrated by Definition

Time to get positive. I was asking Andrew whether it’s true that I have the right coverage in Bayesian posterior intervals if I generate the parameters from the prior and the data from the parameters. He replied that yes indeed that is true, and directed me to: Cook, S.R., Gelman, A. and Rubin, D.B. 2006. […]

## Stacking, pseudo-BMA, and AIC type weights for combining Bayesian predictive distributions

This post is by Aki. We have often been asked in the Stan user forum how to do model combination for Stan models. Bayesian model averaging (BMA) by computing marginal likelihoods is challenging in theory and even more challenging in practice using only the MCMC samples obtained from the full model posteriors. Some users have […]

## “Scalable Bayesian Inference with Hamiltonian Monte Carlo” (Michael Betancourt’s talk this Thurs at Columbia)

Scalable Bayesian Inference with Hamiltonian Monte Carlo Despite the promise of big data, inferences are often limited not by sample size but rather by systematic effects. Only by carefully modeling these effects can we take full advantage of the data—big data must be complemented with big models and the algorithms that can fit them. One […]

## Ensemble Methods are Doomed to Fail in High Dimensions

Ensemble methods [cat picture] By ensemble methods, I (Bob, not Andrew) mean approaches that scatter points in parameter space and then make moves by inteprolating or extrapolating among subsets of them. Two prominent examples are: Ter Braak’s differential evolution Goodman and Weare’s walkers There are extensions and computer implementations of these algorithms. For example, […]

## Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data

After three years, we finally have an updated version of our “EP as a way of life” paper. Authors are Andrew Gelman, Aki Vehtari, Pasi Jylänki, Tuomas Sivula, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John Cunningham, David Schiminovich, and Christian Robert. Aki deserves credit for putting this all together into a coherent whole. Here’s the […]

## A fistful of Stan case studies: divergences and bias, identifying mixtures, and weakly informative priors

Following on from his talk at StanCon, Michael Betancourt just wrote three Stan case studies, all of which are must reads: Diagnosing Biased Inference with Divergences: This case study discusses the subtleties of accurate Markov chain Monte Carlo estimation and how divergences can be used to identify biased estimation in practice. Identifying Bayesian Mixture […]

## Facebook’s Prophet uses Stan

Sean Taylor, a research scientist at Facebook and Stan user, writes: I wanted to tell you about an open source forecasting package we just released called Prophet: I thought the readers of your blog might be interested in both the package and the fact that we built it on top of Stan. Under the hood, […]

## Research fellow, postdoc, and PhD positions in probabilistic modeling and machine learning in Finland

Probabilistic modeling and machine learning are strong in Finland. Now is your opportunity to join us in this cool country! There are several postdoc and research fellow positions open in probabilistic machine learning in Aalto University and University of Helsinki (deadline Marh 19). Some of the topics are related also to probabilistic programming and Stan […]

## Lasso regression etc in Stan

[cat picture] Someone on the users list asked about lasso regression in Stan, and Ben replied: In the rstanarm package we have stan_lm(), which is sort of like ridge regression, and stan_glm() with family = gaussian and prior = laplace() or prior = lasso(). The latter estimates the shrinkage as a hyperparameter while the former […]

## HMMs in Stan? Absolutely!

I was having a conversation with Andrew that went like this yesterday: Andrew: Hey, someone’s giving a talk today on HMMs (that someone was Yang Chen, who was giving a talk based on her JASA paper Analyzing single-molecule protein transportation experiments via hierarchical hidden Markov models). Maybe we should add some specialized discrete modules to […]

## Thanks for attending StanCon 2017!

Thank you all for coming and making the first Stan Conference a success! The organizers were blown away by how many people came to the first conference. We had over 150 registrants this year! StanCon 2017 Video The organizers managed to get a video stream on YouTube: https://youtu.be/DJ0c7Bm5Djk. We have over 1900 views since StanCon! (We lost […]

## Come and work with us!

Stan is an open-source, state-of-the-art probabilistic programming language with a high-performance Bayesian inference engine written in C++. Stan had been successfully applied to modeling problems with hundreds of thousands of parameters in fields as diverse as econometrics, sports analytics, physics, pharmacometrics, recommender systems, political science, and many more. Research using Stan has been featured in […]