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Bayes, Jeffreys, prior distributions, and the philosophy of statistics
Christian Robert, Nicolas Chopin, and Judith Rousseau wrote this article that will appear in Statistical Science with various discussions, including mine. I hope those of you who are interested in the foundations of statistics will read this. Sometimes I feel...
"A gift to the audience rather than a plea for attention": Brad Paley's tips on encouraging seminar participants to ask so many damn questions you have to tell them to shut the heck up already so you can hear the rest of the damn talk
A couple days ago I asked what was it that Brad Paley did to get such active participation in his seminar. I can get active participation, but it takes work: I have to ask students to work in pairs to...
Two kinds of book
One of the things Brad Paley talked about the other day was the computer program he used to make a visualization of the text of Alice in Wonderland [link fixed]. (Click on the "Alice in Wonderland" link; it's really cool.)...
W. Bradford Paley's talk and, more generally, what should I do to encourage audience partipation when I speak?
Programmer/designer W. Bradford Paley spoke yesterday for the data visualization group here at Columbia. He gave an amazing talk, one of the best I've ever seen. One reason I say this is that about half the talk was devoted to...
Radford Neal's blog
Radford's a leading researcher in statistical computing. He started a new blog. Radford writes: Many of my technical posts will point out flaws in research, methods, and tools that are commonly used. Such negative comments are essential to the scientific...
Bayesian prediction with high-order interactions!!
Longhai Li did a really cool Ph.D. thesis (under the supervision of Radford Neal) on computing for models with deep interactions. The website containing all stuff about this software, including the R packages, documentations and references, is here and here....
My class this spring on applied Bayesian statistical computing
I had various course titles floating around: my course at Columbia this spring is officially called Applied Statistics, and I had promised people that it would cover Bayesian statistics. At Harvard they asked me to teach Statistical Computing, but I...
Convergent interviewing and Markov chain simulation
Bill Harris writes,...
Smoothing analysis of variance and extending the definition of degrees of freedom
There's some cool and (possibly) important stuff in Yue Cui's dissertation summary (under the supervision of Jim Hodges and Brad Carlin at University of Minnesota biostat). The short story is that, for reasons of substantive modeling as well as prediction,...
The proliferation of MCMC (and, for that matter, optimization) methods: is there an underlying unity, or does that not make sense?
Radford is speaking in the statistics seminar on Monday 11 Dec (noon at 903 Social Work Bldg, for you locals): Constructing Efficient MCMC Methods Using Temporary Mapping and Caching I [Radford] describe two general methods for obtaining efficient Markov chain...
Sample size and self-efficiency
Xiao-Li Meng is speaking this Friday 2pm in the biostatistics seminar (14th Floor, Room 240, Presbyterian Hospital Bldg, 622 West 168th Street). Here's the abstract: One of the most frequently asked questions in statistical practice, and indeed in general quantitative...
Why hasn't hybrid Monte Carlo caught on?
Hybrid Monte Carlo is not a new energy-efficient auto race. It's a computational method developed by physicists to improve the efficiency of random-walk simulation (i.e., the Metropolis algorithm) by adding auxiliary variables that characterize the "momentum" of the simulation path....
More on burn-in for iterative simulation
Following my discussion with Radford (see the comments of this recent entry), I had this brief back-and-forth with Bob O'Hara regarding adaptive updating and burn-in in Bugs. Me: I want to automatically set all adaptive phases to the burnin period....
Adaptive Metropolis algorithms
Christophe Andrieu gave a talk at IceBugs on adaptive MCMC for Bugs. I wasn't able to attend the meeting, but the presentation looks reasonable to me. Right now, one of my problems with Bugs is that sometimes it crashes--I think...
Free advice: you get what you pay for [statistics edition]
Gregg Keller asks, When setting up a regression model with no obvious hierarchical structure, with normal distribution priors for all the coefficients (where the normal distribution's mean and variance are also defined by a prior distribution), does it make sense...
Bayes and parsimony
Check out Peter Grunwald's long comment on this entry on Bayes and parsimony. He has some interesting things to say, most notably in cautioning that Bayesian inference will not necessarily get you to the true model, or even close to...
Against parsimony, again
The comments to a recent entry on "what is a Bayesian" moved toward a discussion of parsimony in modeling (also noted here). I'd like to comment on something that Dan Navarro wrote. First I'll repeat Dan's comments, then give my...
Against parsimony
A lot has been written in statistics about "parsimony"--that is, the desire to explain phenomena using fewer parameters--but I've never seen any good general justification for parsimony. (I don't count "Occam's Razor," or "Ockham's Razor," or whatever, as a justification....
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