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Archive of posts filed under the Bayesian Statistics category.

Why do we communicate probability calculations so poorly, even when we know how to do it better?

Haynes Goddard writes: I thought to do some reading in psychology on why Bayesian probability seems so counterintuitive, and making it difficult for many to learn and apply. Indeed, that is the finding of considerable research in psychology. It turns out that it is counterintuitive because of the way it is presented, following no doubt […]

New research in tuberculosis mapping and control

Mapping and control. Or, as we would say, descriptive and causal inference. Jon Zelner informs os about two ongoing research projects: 1. TB Hotspot Mapping: Over the summer, I [Zelner] put together a really simple R package to do non-parametric disease mapping using the distance-based mapping approach developed by Caroline Jeffery and Al Ozonoff at […]

Comparison of Bayesian predictive methods for model selection

This post is by Aki We mention the problem of bias induced by model selection in A survey of Bayesian predictive methods for model assessment, selection and comparison, in Understanding predictive information criteria for Bayesian models, and in BDA3 Chapter 7, but we haven’t had a good answer how to avoid that problem (except by […]

But when you call me Bayesian, I know I’m not the only one

Textbooks on statistics emphasize care and precision, via concepts such as reliability and validity in measurement, random sampling and treatment assignment in data collection, and causal identification and bias in estimation. But how do researchers decide what to believe and what to trust when choosing which statistical methods to use? How do they decide the […]

Regression: What’s it all about? [Bayesian and otherwise]

Regression: What’s it all about? Regression plays three different roles in applied statistics: 1. A specification of the conditional expectation of y given x; 2. A generative model of the world; 3. A method for adjusting data to generalize from sample to population, or to perform causal inferences. We could also include prediction, but I […]

The publication of one of my pet ideas: Simulation-efficient shortest probability intervals

In a paper to appear in Statistics and Computing, Ying Liu, Tian Zheng, and I write: Bayesian highest posterior density (HPD) intervals can be estimated directly from simulations via empirical shortest intervals. Unfortunately, these can be noisy (that is, have a high Monte Carlo error). We derive an optimal weighting strategy using bootstrap and quadratic […]

Adiabatic as I wanna be: Or, how is a chess rating like classical economics?

Chess ratings are all about change. Did your rating go up, did it go down, have you reached 2000, who’s hot, who’s not, and so on. If nobody’s abilities were changing, chess ratings would be boring, they’d be nothing but a noisy measure, and watching your rating change would be as exciting as watching a […]

Paul Meehl continues to be the boss

Lee Sechrest writes: Here is a remarkable paper, not well known, by Paul Meehl. My research group is about to undertake a fresh discussion of it, which we do about every five or ten years. The paper is now more than a quarter of a century old but it is, I think, dramatically pertinent to […]

Why I don’t use the terms “fixed” and “random” (again)

A couple months ago we discussed this question from Sean de Hoon: In many cross-national comparative studies, mixed effects models are being used in which a number of slopes are fixed and the slopes of one or two variables of interested are allowed to vary across countries. The aim is often then to explain the […]

Bayesian models, causal inference, and time-varying exposures

Mollie Wood writes: I am a doctoral student in clinical and population health research. My dissertation research is on prenatal medication exposure and neurodevelopmental outcomes in children, and I’ve encountered a difficult problem that I hope you might be able to advise me on. I am working on a problem in which my main exposure […]

Interactive demonstrations for linear and Gaussian process regressions

Here’s a cool interactive demo of linear regression where you can grab the data points, move them around, and see the fitted regression line changing. There are various such apps around, but this one is particularly clean: (I’d like to credit the creator but I can’t find any attribution at the link, except that it’s […]

My talk tomorrow (Thurs) at MIT political science: Recent challenges and developments in Bayesian modeling and computation (from a political and social science perspective)

It’s 1pm in room E53-482. I’ll talk about the usual stuff (and some of this too, I guess).

One simple trick to make Stan run faster

Did you know that Stan automatically runs in parallel (and caches compiled models) from R if you do this: source(“http://mc-stan.org/rstan/stan.R”) (editor: deprecated as of RStan 2.7.0; use at your own peril) It’s from Stan core developer Ben Goodrich. This simple line of code has changed my life. A factor-of-4 speedup might not sound like much, […]

Introducing shinyStan

As a project for Andrew’s Statistical Communication and Graphics graduate course at Columbia, a few of us (Michael Andreae, Yuanjun Gao, Dongying Song, and I) had the goal of giving RStan’s print and plot functions a makeover. We ended up getting a bit carried away and instead we designed a graphical user interface for interactively exploring virtually […]

Bayes and doomsday

Ben O’Neill writes: I am a fellow Bayesian statistician at the University of New South Wales (Australia).  I have enjoyed reading your various books and articles, and enjoyed reading your recent article on The Perceived Absurdity of Bayesian Inference.  However, I disagree with your assertion that the “doomsday argument” is non-Bayesian; I think if you read […]

Statistical Significance – Significant Problem?

John Carlin, who’s collaborated on some of my recent work on Type S and Type M errors, prepared this presentation for a clinical audience. It might be of interest to some of you. The ideas and some of the examples should be familiar to regular readers of this blog, but it could be useful to […]

Bayesian survival analysis with horseshoe priors—in Stan!

Tomi Peltola, Aki Havulinna, Veikko Salomaa, and Aki Vehtari write: This paper describes an application of Bayesian linear survival regression . . . We compare the Gaussian, Laplace and horseshoe shrinkage priors, and find that the last has the best predictive performance and shrinks strong predictors less than the others. . . . And here’s […]

Stan Down Under

I (Bob, not Andrew) am in Australia until April 30. I’ll be giving some Stan-related and some data annotation talks, several of which have yet to be concretely scheduled. I’ll keep this page updated with what I’ll be up to. All of the talks other than summer school will be open to the public (the […]

Why I keep talking about “generalizing from sample to population”

Someone publishes some claim, some statistical comparison with “p less than .05” attached to it. My response is: OK, you see this pattern in the sample. Do you think it holds in the population? Why do I ask this? Why don’t I ask the more standard question: Do you really think this result is statistically […]

Total survey error

Erez Shalom writes: It’s election time in Israel and every week several surveys come out trying to predict the ‘mandates’ that each party will get (out of a total of 120). These surveys are historically flakey, and no one takes the ‘sampling error’ they come with seriously, but no one has a good idea of […]