My article with Cosma Shalizi has appeared in the British Journal of Mathematical and Statistical Psychology. I’m so glad this paper has come out. I’d been thinking about writing such a paper for almost 20 years. What got me to actually do it was an invitation a few years ago to write a chapter on [...]
Archive of posts filed under the Bayesian Statistics category.
The new Stan 1.1.1, featuring Gaussian processes!
We just released Stan 1.1.1 and RStan 1.1.1 As usual, you can find download and install instructions at: http://mc-stan.org/ This is a patch release and is fully backward compatible with Stan and RStan 1.1.0. The main thing you should notice is that the multivariate models should be much faster and all the bugs reported for [...]
Economists argue about Bayes
Robert Bell pointed me to this post by Brad De Long on Bayesian statistics, and then I also noticed this from Noah Smith, who wrote: My impression is that although the Bayesian/Frequentist debate is interesting and intellectually fun, there’s really not much “there” there… despite being so-hip-right-now, Bayesian is not the Statistical Jesus. I’m happy [...]
if you’re already using sophisticated non-Bayesian methods such as those of Tibshirani, Efron, and others, that Bayes is more of an option than a revolution. But if you’re coming out of a pure hypothesis testing training, then Bayes can be a true revelation. I think that is one reason that many methodologists in psychology are such avid Bayesians: they find the openness and the directness of the Bayesian approach to be so liberating.
Psychology researcher Gary Marcus points me to this comment he posted regarding popular representations of Bayesian and non-Bayesian statistics. Gary guessed that I’d disagree with him, but I actually thought that what he wrote was pretty reasonable. (Or maybe that’s just my disagreeable nature, that in this case I show my contrarian nature by agreeing [...]
I don’t believe the paper, “Empirical estimates suggest most published medical research is true.” That is, most published medical research may well be true, but I’m not at all convinced by the analysis being used to support this claim.
David Austin pointed me to this article by Leah Jager and Jeffrey Leek. The title is funny but the article is serious: The accuracy of published medical research is critical both for scientists, physicians and patients who rely on these results. But the fundamental belief in the medical literature was called into serious question by [...]
Finite-population Anova calculations for models with interactions
Jim Thomson writes: I wonder if you could provide some clarification on the correct way to calculate the finite-population standard deviations for interaction terms in your Bayesian approach to ANOVA (as explained in your 2005 paper, and Gelman and Hill 2007). I understand that it is the SD of the constrained batch coefficients that is [...]
Participate in a short survey about the weight of evidence provided by statistics
Richard Morey writes: Rink Hoekstra and I are undertaking some research to explore how people use classical statistical results to evaluate the weight of evidence. Bayesians often critique classical techniques for being difficult to interpret in terms of what scientists want to know, but there is clearly information in the statistics themselves. We wonder how [...]
R package for Bayes factors
Richard Morey writes: You and your blog readers may be interested to know that a we’ve released a major new version of the BayesFactor package to CRAN. The package computes Bayes factors for linear mixed models and regression models. Of course, I’m aware you don’t like point-null model comparisons, but the package does more than [...]
Prior Selection for Vector Autoregressions
Brendan Nyhan sends along this paper by Domenico Giannone, Michele Lenza, and Giorgio Primiceri: Vector autoregressions are flexible time series models that can capture complex dynamic interrelationships among macroeconomic variables. However, their dense parameterization leads to unstable inference and inaccurate out-of-sample forecasts, particularly for models with many variables. A solution to this problem is to [...]
Bayesian, Permutable Symmetries
Mike Betancourt sends along this paper. Could be interesting, no? Note the heavy tail on the CDF in Figure 3, exhibiting weakened median time since 1999. And, as you can see from the bibliography, the work draws on a variety of sources:
A important new survey of Bayesian predictive methods for model assessment, selection and comparison
Aki Vehtari and Janne Ojanen just published a long paper that begins: To date, several methods exist in the statistical literature for model assessment, which purport themselves specifically as Bayesian predictive methods. The decision theoretic assumptions on which these methods are based are not always clearly stated in the original articles, however. The aim of [...]
New book by Stef van Buuren on missing-data imputation looks really good!
Ben points us to a new book, Flexible Imputation of Missing Data. It’s excellent and I highly recommend it. Definitely worth the $89.95. Van Buuren’s book is great even if you don’t end up using the algorithm described in the book (I actually like their approach but I do think there are some limitations with [...]
Two reviews of Nate Silver’s new book, from Kaiser Fung and Cathy O’Neil
People keep asking me what I think of Nate’s book, and I keep replying that, as a blogger, I’m spoiled. I’m so used to getting books for free that I wouldn’t go out and buy a book just for the purpose of reviewing it. (That reminds me that I should post reviews of some of [...]
The Case for More False Positives in Anti-doping Testing
Kaiser Fung was ahead of the curve on Lance Armstrong: The media has gotten the statistics totally backwards. On the one hand, they faithfully report the colorful stories of athletes who fail drug tests pleading their innocence. (I have written about the Spanish cyclist Alberto Contador here.) On the other hand, they unquestioningly report athletes [...]
Feedback on my Bayesian Data Analysis class at Columbia
In one of the final Jitts, we asked the students how the course could be improved. Some of their suggestions would work, some would not. I’m putting all the suggestions below, interpolating my responses. (Overall, I think the course went well. Please remember that the remarks below are not course evaluations; they are answers to [...]
Yes, checking calibration of probability forecasts is part of Bayesian statistics
Yes, checking calibration of probability forecasts is part of Bayesian statistics. At the end of this post are three figures from Chapter 1 of Bayesian Data Analysis illustrating empirical evaluation of forecasts. But first the background. Why am I bringing this up now? It’s because of something Larry Wasserman wrote the other day:
Readings for a two-week segment on Bayesian modeling?
Michael Landy writes: I’m in Psych and Center for Neural Science and I’m teaching a doctoral course this term in methods in psychophysics (never mind the details) at the tail end of which I’m planning on at least 2 lectures on Bayesian parameter estimation and Bayesian model comparison. So far, all the readings I have [...]
Stantastic!
Richard McElreath writes: I’ve been translating a few ongoing data analysis projects into Stan code, mostly with success. The most important for me right now has been a hierarchical zero-inflated gamma problem. This a “hurdle” model, in which a bernoulli GLM produces zeros/nonzeros, and then a gamma GLM produces the nonzero values, using varying effects [...]
Thinking like a statistician (continuously) rather than like a civilian (discretely)
John Cook writes: When I hear someone say “personalized medicine” I want to ask “as opposed to what?” All medicine is personalized. If you are in an emergency room with a broken leg and the person next to you is lapsing into a diabetic coma, the two of you will be treated differently. The aim [...]
I don’t like this cartoon
Some people pointed me to this: I am happy to see statistical theory and methods be a topic in popular culture, and of course I’m glad that, contra Feller, the Bayesian is presented as the hero this time, but . . . . I think the lower-left panel of the cartoon unfairly misrepresents frequentist statisticians. [...]
The anti-Bayesian moment and its passing
Xian and I respond to the four discussants of our paper, “Not only defended but also applied”: The perceived absurdity of Bayesian inference.” Here’s the abstract of our rejoinder: Over the years we have often felt frustration, both at smug Bayesians—in particular, those who object to checking of the fit of model to data, either [...]
Poll aggregation and election forecasting
At the sister blog, Henry writes about poll averaging and election forecasts. Henry writes that “These models need to crunch lots of polls, at the state and national level, if they’re going to provide good predictions.” Actually, you can get reasonable predictions from national-level forecasting models plus previous state-level election results, then when the election [...]
Let’s try this: Instead of saying, “The probability is 75%,” say “There’s a 25% chance I’m wrong”
I recently wrote about the difficulty people have with probabilities, in this case the probability that Obama wins the election. If the probability is reported as 70%, people think Obama is going to win. Actually, though, it just means that Obama is predicted to get about 50.8% of the two-party vote, with an uncertainty of [...]
Statistical methods that work in some settings but not others
David Hogg pointed me to this post by Larry Wasserman: 1. The Horwitz-Thompson estimator satisfies the following condition: for every , where — the parameter space — is the set of all functions . (There are practical improvements to the Horwitz-Thompson estimator that we discussed in our earlier posts but we won’t revisit those here.) 2. A Bayes estimator [...]
It not necessary that Bayesian methods conform to the likelihood principle
Bayesian inference, conditional on the model and data, conforms to the likelihood principle. But there is more to Bayesian methods than Bayesian inference. See chapters 6 and 7 of Bayesian Data Analysis for much discussion of this point. It saddens me to see that people are still confused on this issue.