Can we implement these in Stan? Marginally specified priors for non-parametric Bayesian estimation (by David Kessler, Peter Hoff, and David Dunson): Prior specification for non-parametric Bayesian inference involves the difficult task of quantifying prior knowledge about a parameter of high, often infinite, dimension. A statistician is unlikely to have informed opinions about all aspects of […]

**Bayesian Statistics**category.

## “The Warriors suck”: A Bayesian exploration

A basketball fan of my close acquaintance woke up Wednesday morning and, upon learning the outcome of the first games of the NBA season, announced that “The Warriors suck.” Can we answer this question? To put it more precisely, how much information is supplied by that first-game-of-season blowout? Speaking Bayesianly, who much should we adjust […]

## “Generic and consistent confidence and credible regions”

Christian Bartels sends along this paper, which begins: A generic, consistent, efficient and exact method is proposed for set selection. The method is generic in that its definition and implementation uses only the likelihood function. The method is consistent in that the same criterion is used to select confidence and credible sets making the two […]

## Yes, despite what you may have heard, you can easily fit hierarchical mixture models in Stan

There was some confusion on the Stan list that I wanted to clear up, having to do with fitting mixture models. Someone quoted this from John Kruschke’s book, Doing Bayesian Data Analysis: The lack of discrete parameters in Stan means that we cannot do model comparison as a hierarchical model with an indexical parameter at […]

## Practical Bayesian model evaluation in Stan and rstanarm using leave-one-out cross-validation

Our (Aki, Andrew and Jonah) paper Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC was recently published in Statistics and Computing. In the paper we show why it’s better to use LOO instead of WAIC for model evaluation how to compute LOO quickly and reliably using the full posterior sample how Pareto smoothing importance […]

## Avoiding model selection in Bayesian social research

One of my favorites, from 1995. Don Rubin and I argue with Adrian Raftery. Here’s how we begin: Raftery’s paper addresses two important problems in the statistical analysis of social science data: (1) choosing an appropriate model when so much data are available that standard P-values reject all parsimonious models; and (2) making estimates and […]

## Mathematica, now with Stan

Vincent Picaud developed a Mathematica interface to Stan: MathematicaStan You can find everything you need to get started by following the link above. If you have questions, comments, or suggestions, please let us know through the Stan user’s group or the GitHub issue tracker. MathematicaStan interfaces to Stan through a CmdStan process. Stan programs are […]

## Webinar: Introduction to Bayesian Data Analysis and Stan

This post is by Eric. We are starting a series of free webinars about Stan, Bayesian inference, decision theory, and model building. The first webinar will be held on Tuesday, October 25 at 11:00 AM EDT. You can register here. Stan is a free and open-source probabilistic programming language and Bayesian inference engine. In this […]

## Is it fair to use Bayesian reasoning to convict someone of a crime?

Ethan Bolker sends along this news article from the Boston Globe: If it doesn’t acquit, it must fit Judges and juries are only human, and as such, their brains tend to see patterns, even if the evidence isn’t all there. In a new study, researchers first presented people with pieces of evidence (a confession, an […]

## Tenure Track Professor in Machine Learning, Aalto University, Finland

Posted by Aki. I promise that next time I’ll post something else than a job advertisement, but before that here’s another great opportunity to join Aalto Univeristy where I work, too. “We are looking for a professor to either further strengthen our strong research fields, with keywords including statistical machine learning, probabilistic modelling, Bayesian inference, […]

## Mister P can solve problems with survey weighting

It’s tough being a blogger who’s expected to respond immediately to topics in his area of expertise. For example, here’s Scott “fraac” Adams posting on 8 Oct 2016, post titled “Why Does This Happen on My Vacation? (The Trump Tapes).” After some careful reflection, Adams wrote, “My prediction of a 98% chance of Trump winning […]

## The real dogs and the stat-dogs

One of the earliest examples of simulation-based model checking in statistics comes from the 1954 book by Robert Bush and Frederick Mosteller, Stochastic Models for Learning. They fit a probability model to data on dogs being shocked in a research lab (yeah, I know, not an experiment that would be done today). Then they simulate […]

## 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 […]

## He wants to get started on Bayes

Mathew Mercuri writes: I am interested in learning how to work in a Bayesian world. I have training in a frequentist approach, specifically from an applied health scientist/epidemiologist approach. However, while i teach courses in applied statistics, I am not particularly savvy with heavy statistical mathematics, so I am a bit worried bout how to […]

## Trump +1 in Florida; or, a quick comment on that “5 groups analyze the same poll” exercise

Nate Cohn at the New York Times arranged a comparative study on a recent Florida pre-election poll. He sent the raw data to four groups (Charles Franklin; Patrick Ruffini; Margie Omero, Robert Green, Adam Rosenblatt; and Sam Corbett-Davies, David Rothschild, and me) and asked each of us to analyze the data how we’d like to […]

## “Crimes Against Data”: My talk at Ohio State University this Thurs; “Solving Statistics Problems Using Stan”: My talk at the University of Michigan this Fri

Crimes Against Data Statistics has been described as the science of uncertainty. But, paradoxically, statistical methods are often used to create a sense of certainty where none should exist. The social sciences have been rocked in recent years by highly publicized claims, published in top journals, that were reported as “statistically significant” but are implausible […]

## Let’s play Twister, let’s play Risk

Alex Terenin, Dan Simpson, and David Draper write: Some months ago we shared with you an arxiv draft of our paper, Asynchronous Distributed Gibbs Sampling. Through comments we’ve received, for which we’re highly grateful, we came to understand that (a) our convergence proof was wrong, and (b) we actually have two algorithms, one exact and […]

## Solving Statistics Problems Using Stan (my talk at the NYC chapter of the American Statistical Association)

Here’s the announcement: Solving Statistics Problems Using Stan Stan is a free and open-source probabilistic programming language and Bayesian inference engine. In this talk, we demonstrate the use of Stan for some small fun problems and then discuss some open problems in Stan and in Bayesian computation and Bayesian inference more generally. It’s next Tues, […]

## Bayesian Statistics Then and Now

I happened to recently reread this article of mine from 2010, and I absolutely love it. I don’t think it’s been read by many people—it was published as one of three discussions of an article by Brad Efron in Statistical Science—so I wanted to share it with you again here. This is the article where […]