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Archive of posts filed under the Bayesian Statistics category.

Stunning breakthrough: Using Stan to map cancer screening!

Paul Alper points me to this article, Breast Cancer Screening, Incidence, and Mortality Across US Counties, by Charles Harding, Francesco Pompei, Dmitriy Burmistrov, Gilbert Welch, Rediet Abebe, and Richard Wilson. Their substantive conclusion is there’s too much screening going on, but here I want to focus on their statistical methods: Spline methods were used to […]

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 […]

Kéry and Schaub’s Bayesian Population Analysis Translated to Stan

Hiroki ITÔ (pictured) has done everyone a service in translating to Stan the example models [update: only chapters 3–9 so far, not the whole book; the rest are in the works] from Marc Kéry and Michael Schaub (2012) Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective. Academic Press. You can find the code in our […]

Jim Albert’s Baseball Blog

Jim Albert has a baseball blog: Baseball with R I sent a link internally to people I knew were into baseball, to which Andrew replied, “I agree that it’s cool that he doesn’t just talk, he has code.” (No kidding—the latest post as of writing this was on an R package to compute value above […]

My talk Fri 1pm at the University of Chicago

It’s the Data Science and Public Policy colloquium, and they asked me to give my talk, Little Data: How Traditional Statistical Ideas Remain Relevant in a Big-Data World. Here’s the abstract: “Big Data” is more than a slogan; it is our modern world in which we learn by combining information from diverse sources of varying […]

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 […]

rstanarm and more!

Ben Goodrich writes: The rstanarm R package, which has been mentioned several times on stan-users, is now available in binary form on CRAN mirrors (unless you are using an old version of R and / or an old version of OSX). It is an R package that comes with a few precompiled Stan models — […]

Plausibility vs. probability, prior distributions, and the garden of forking paths

I’ll start off this blog on the first work day of the new year with an important post connecting some ideas we’ve been lately talking a lot about. Someone rolls a die four times, and he tells you he got the numbers 1, 4, 3, 6. Is this a plausible outcome? Sure. Is is probable? […]

He’s skeptical about Neuroskeptic’s skepticism

Jim Delaney writes: Through a link in the weekend reads on Retraction Watch, I read Neuroskeptic’s post-publication peer review of a study on an antidepressant application of the drug armodafinil. Neuroskeptic’s main criticism is that he/she feels that a “conclusion” in the abstract is misleading, “… Adjunctive armodafinil 150 mg/day reduced depressive symptoms associated with […]

Gathering of philosophers and physicists unaware of modern reconciliation of Bayes and Popper

Hiro Minato points us to a news article by physicist Natalie Wolchover entitled “A Fight for the Soul of Science.” I have no problem with most of the article, which is a report about controversies within physics regarding the purported untestability of physics models such as string theory (as for example discussed by my Columbia […]

Bayesian decision analysis for the drug-approval process (NSFW)

Bill Jefferys points me to a paper, “Is the FDA Too Conservative or Too Aggressive?: A Bayesian Decision Analysis of Clinical Trial Design,” by Vahid Montazerhodjat and Andrew Lo. Here’s the abstract: Implicit in the drug-approval process is a trade-off between Type I and Type II error. We propose using Bayesian decision analysis (BDA) to […]

Jökull Snæbjarnarson writes . . .

Wow! After that name, anything that follows will be a letdown. But we’ll answer his or her question anyway. So here goes. Jökull Snæbjarnarson writes: I’m fitting large bayesian regression models in Stan where I have many parameters. Having fitted a model and some of the “beta” coefficients HDI’s, where beta is the beta in […]

1 cool trick for defining conditional probability

Hi, this one comes up from time to time so I thought I’d devote a whole post to it. The question is: what is conditional probability? And here’s what I wrote: Everyone agrees that P(A,B) = P(A|B)*P(B). The question is, what comes first? In traditional probability textbooks, P(A,B) is defined first, then P(A|B) is defined […]

More on prior distributions for climate sensitivity

In response to this post the other day on prior distributions for climate sensitivity, Nicholas Lewis wrote in: Your post refers to comments I made at ATTP’s blog about the use of Jeffreys’ prior in estimating climate sensitivity. I would like to explain why, in some but not all cases, the Jeffreys’ prior for estimating […]

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 […]

Hierarchical modeling when you have only 2 groups: I still think it’s a good idea, you just need an informative prior on the group-level variation

Dan Chamberlain writes: I am working on a Bayesian analysis of some data from a randomized controlled trial comparing two different drugs for treating seizures in children. I have been using your book as a resource and I have a question about hierarchical modeling. If you have the time, I would greatly appreciate any advice […]

Probabilistic Integration

Mark Girolami sends along a new paper by Francois-Xavier Briol, Chris Oates, Michael Osborne, Dino Sejdinovic, and himself. The idea is to consider numerical integration as a statistical problem, to say that the integral being estimated is an unknown parameter and then to perform inference about it. This is related to ideas of Xiao-Li Meng, […]

Use of Jeffreys prior in estimating climate sensitivity

William Morris writes: A discussion of the use of Bayesian estimation in calculating climate sensitivity (to doubled CO2) occurred recently in the comments at the And Then There’s Physics (ATTP) blog. One protagonist, ‘niclewis’, a well known climate sensitivity researcher, uses the Jeffreys prior in his estimations. His estimations are always at the low end […]

Judea Pearl and I briefly discuss extrapolation, causal inference, and hierarchical modeling

OK, I guess it looks like the Buzzfeed-style headlines are officially over. Anyway, Judea Pearl writes: I missed the discussion you had here about Econometrics: Instrument locally, extrapolate globally, which also touched on my work with Elias Bareinboim. So, please allow me to start a new discussion about extrapolation and external validity. First, two recent […]