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Archive of posts filed under the Bayesian Statistics category.

Constructing an informative prior using meta-analysis

Chris Guure writes: I am trying to construct an informative prior by synthesizing or collecting some information from literature (meta-analysis) and then to apply that to a real data set (it is longitudinal data) for over 20 years follow-up. In constructing the prior using the meta-analysis data, the issue of publication bias came up. I […]

Performing design calculations (type M and type S errors) on a routine basis?

Somebody writes writes: I am conducting a survival analysis (median follow up ~10 years) of subjects who enrolled on a prospective, non-randomized clinical trial for newly diagnosed multiple myeloma. The data were originally collected for research purposes and specifically to determine PFS and OS of the investigational regimen versus historic controls. The trial has been […]

“Can you change your Bayesian prior?”

Deborah Mayo writes: I’m very curious as to how you would answer this for subjective Bayesians, at least. I found this section of my book showed various positions, not in agreement. I responded on her blog: As we discuss in BDA and elsewhere, one can think of one’s statistical model, at any point in time, […]

Data-analysis assignments for BDA class?

In my Bayesian data analysis class this fall, I’m planning on doing some lecturing and class discussion, but the core of the course will be weekly data-analysis assignments where they do applied statistics using Stan (to fit models) and R (to pre-process the data and post-process the inferences). So, I need a bunch of examples. […]

Macartan Humphreys on the Worm Wars

My Columbia political science colleague shares “What Has Been Learned from the Deworming Replications: A Nonpartisan View”: Last month there was another battle in a dispute between economists and epidemiologists over the merits of mass deworming.1 In brief, economists claim there is clear evidence that cheap deworming interventions have large effects on welfare via increased […]

My 2 classes this fall

Stat 6103, Bayesian Data Analysis Modern Bayesian methods offer an amazing toolbox for solving science and engineering problems. We will go through the book Bayesian Data Analysis and do applied statistical modeling using Stan, using R (or Python or Julia if you prefer) to preprocess the data and postprocess the analysis. We will also discuss […]

ShinyStan v2.0.0

For those of you not familiar with ShinyStan, it is a graphical user interface for exploring Stan models (and more generally MCMC output from any software). For context, here’s the post on this blog first introducing ShinyStan (formerly shinyStan) from earlier this year. ShinyStan v2.0.0 released ShinyStan v2.0.0 is now available on CRAN. This is […]

Fitting a multilevel model

Cui Yang writes: I have a question about the use of BRT (Boosting regression tree). I am planning to write an article about the effects of soil fauna and understory fine roots on forest soil organic carbon. The experiment was conducted in a subtropical forest area in China. There were 16 blocks each with 5 […]

Monte Carlo and the Holy Grail

On 31 Dec 2010, someone wrote in: A British Bayesian curiosity: Adrian Smith has just been knighted, and so becomes Sir Adrian. He can’t be the first Bayesian knight, as Harold Jeffreys was Sir Harold. I replied by pointing to this discussion from 2008, and adding: Perhaps Spiegelhalter can be knighted next. Or maybe Ripley! […]

Classifying causes of death using “verbal autopsies”

Tyler McCormick sent along this paper, “Probabilistic Cause-of-death Assignment using Verbal Autopsies,” coauthored with Zehang Li, Clara Calvert, Amelia Crampin, Kathleen Kahn, and Samuel Clark: In areas without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such areas the majority of deaths occur outside hospitals […]

The secret to making a successful conference presentation

JSM (the Joint Statistical Meetings) are coming up soon, and Jiqiang’s giving a talk on Stan. Here’s the advice I gave him: in 20 minutes, something like this: – What is Stan? – Where does Stan work well? – Current and future Stan research. For JSM audience it could be good to spend some time […]

When does Bayes do the job?

E. J. writes: I’m writing a paper where I discuss one of the advantages of Bayesian inference, namely that it scales up to complex problems where maximum likelihood would simply be unfeasible or unattractive. I have an example where 2000 parameters are estimated in a nonlinear hierarchical model; MLE would not fare well in this […]

Pro Publica’s new Surgeon Scorecards

Skyler Johnson writes: You should definitely weigh in on this… Pro Publica created “Surgeon Scorecards” based upon risk adjusted surgery compilation rates. They used hierarchical modeling via the lmer package in R. For detailed methodology, click the methodology “how we calculated complications” link, then atop that next page click on the detailed methodology to download […]

BREAKING . . . Kit Harrington’s height

Rasmus “ticket to” Bååth writes: I heeded your call to construct a Stan model of the height of Kit “Snow” Harrington. The response on Gawker has been poor, unfortunately, but here it is, anyway. Yeah, I think the people at Gawker have bigger things to worry about this week. . . . Here’s Rasmus’s inference […]

Measurement is part of design

The other day, in the context of a discussion of an article from 1972, I remarked that the great statistician William Cochran, when writing on observational studies, wrote almost nothing about causality, nor did he mention selection or meta-analysis. It was interesting that these topics, which are central to any modern discussion of observational studies, […]

New papers on LOO/WAIC and Stan

Aki, Jonah, and I have released the much-discussed paper on LOO and WAIC in Stan: Efficient implementation of leave-one-out cross-validation and WAIC for evaluating fitted Bayesian models. We (that is, Aki) now recommend LOO rather than WAIC, especially now that we have an R function to quickly compute LOO using Pareto smoothed importance sampling. In […]

Prior information, not prior belief

The prior distribution p(theta) in a Bayesian analysis is often presented as a researcher’s beliefs about theta. I prefer to think of p(theta) as an expression of information about theta. Consider this sort of question that a classically-trained statistician asked me the other day: If two Bayesians are given the same data, they will come […]

Don’t do the Wilcoxon

The Wilcoxon test is a nonparametric rank-based test for comparing two groups. It’s a cool idea because, if data are continuous and there is no possibility of a tie, the reference distribution depends only on the sample size. There are no nuisance parameters, and the distribution can be tabulated. From a Bayesian point of view, […]

Short course on Bayesian data analysis and Stan 19-21 July in NYC!

Bob Carpenter, Daniel Lee, and I are giving a 3-day short course in two weeks. Before class everyone should install R, RStudio and RStan on their computers. If problems occur please join the stan-users group and post any questions. It’s important that all participants get Stan running and bring their laptops to the course. Class […]

“Why should anyone believe that? Why does it make sense to model a series of astronomical events as though they were spins of a roulette wheel in Vegas?”

Deborah Mayo points us to a post by Stephen Senn discussing various aspects of induction and statistics, including the famous example of estimating the probability the sun will rise tomorrow. Senn correctly slams a journalistic account of the math problem: The canonical example is to imagine that a precocious newborn observes his first sunset, and […]