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Archive of posts filed under the Bayesian Statistics category.

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 […]

Where does Mister P draw the line?

Bill Harris writes: Mr. P is pretty impressive, but I’m not sure how far to push him in particular and MLM [multilevel modeling] in general. Mr. P and MLM certainly seem to do well with problems such as eight schools, radon, or the Xbox survey. In those cases, one can make reasonable claims that the […]

Interpreting posterior probabilities in the context of weakly informative priors

Nathan Lemoine writes: I’m an ecologist, and I typically work with small sample sizes from field experiments, which have highly variable data. I analyze almost all of my data now using hierarchical models, but I’ve been wondering about my interpretation of the posterior distributions. I’ve read your blog, several of your papers (Gelman and Weakliem, […]

How tall is Kit Harrington? Stan wants to know.

We interrupt our regularly scheduled programming for a special announcement. Madeleine Davies writes: “Here are some photos of Kit Harington. Do you know how tall he is?” I’m reminded, of course, of our discussion of the height of professional tall person Jon Lee Anderson: Full Bayes, please. I can’t promise publication on Gawker, but I’ll […]

“Best Linear Unbiased Prediction” is exactly like the Holy Roman Empire

Dan Gianola pointed me to this article, “One Hundred Years of Statistical Developments in Animal Breeding,” coauthored with Guilherme Rosa, which begins: Statistical methodology has played a key role in scientific animal breeding. Approximately one hundred years of statistical developments in animal breeding are reviewed. Some of the scientific foundations of the field are discussed, […]

The posterior distribution of the likelihood ratio as a summary of evidence

Gabriel Marinello writes: I am a PhD student in Astrophysics and am writing this email to you because an enquiry about point null hypothesis testing (H0: Theta = Theta0 and H1: Theta != Theta0) in a bayesian context and I think that your pragmatic stance would be helpful. In Astrophysics is not rare to find […]

A quick one

Fabio Rojas asks: Should I do Bonferroni adjustments? Pros? Cons? Do you have a blog post on this? Most social scientists don’t seem to be aware of this issue. My short answer is that if you’re fitting mutlilevel models, I don’t think you need multiple comparisons adjustments; see here.