I’ve been working through Gelman et al.’s otherwise excellent Bayesian Data Analysis and it’s going reasonably well. My statistics is a little bit rusty so it’s taken time to work through all of the exercises and really understand what’s going on. But I say “otherwise ... [Read more...]

I asked my research group recently what they wished they had learned before they started work on a PhD. Here are some of the responses. More mathematics. Particular topics they named included real analysis, functional analysis, measure theory, algebra, linear algebra. That would have been my response also. I still ... [Read more...]

A Bernoulli process is a sequence of Bernoulli trials (the realization of n binary random variables), taking two values (0/1, Heads/Tails, Boy/Girl, etc…). It is often used in teaching introductory probability/statistics classes about the binomial distribution. When visualizing a Bernoulli process, it is common to use a binary ... [Read more...]

Avril Coghlan, a lecturer at University College Cork in Ireland, has written and made available for free three books ideal for students or practitioners new to R who want to use it for multivariate analysis, time series analysis or biomedical statistics. Each book begins with practical advice for installing and ... [Read more...]

Erkan Buzbas sent me an email about his webpage (operated as a wordpress blog) on ABC. It contains different items of information on ABC research and an hopefully growing list of references. After Scott Sisson’s tweet on ABC_research (latest news: two ABC sessions in ISBA 20122, Kyoto),  here comes ... [Read more...]

Yes, yet another Bayesian textbook: Ioannis Ntzoufras’ Bayesian modeling using WinBUGS was published in 2009 and it got an honourable mention at the 2009 PROSE Award. (Nice acronym for a book award! All the mathematics books awarded that year were actually statistics books.) Bayesian modeling using WinBUGS is rather similar to the ... [Read more...]

The Gaussian vector is extremely interesting since it remains Gaussian when conditioning. More precisely, if is a Gaussian random vector, then the conditional distribution of is also Gaussian. Further, it is possible to derive explicitly the cova... [Read more...]

As supplementary material to the ABC paper we just arXived, here is the R code I used to produce the Bayes factor comparisons between summary statistics in the normal versus Laplace example. (Warning: running the R code takes a while!) Filed under: R, Statistics, University life Tagged: ABC, Bayesian model ... [Read more...]

Introduction I’ve recently been working with a couple of large, extremely sparse data sets in R. This has pushed me to spend some time trying to master the CRAN packages that support sparse matrices. This post describes three of them: the Matrix, slam and glmnet packages. The first two ... [Read more...]

Here is [yet!] another Bayesian textbook that appeared recently. I read it in the past few days and, despite my obvious biases and prejudices, I liked it very much! It has a lot in common (at least in spirit) with our Bayesian Core, which may explain why I feel so ... [Read more...]

Everyday, a poor soul tries to understand copulas by reading the corresponding Wikipedia page, and gives up in despair. The incomprehensible mess that one finds there gives the impression that copulas are about as accessible as tensor theory, which is a shame, because they are actually a very nice tool. ... [Read more...]

For anyone who wants to estimate linear or nonlinear mixed-effects models (aka random-effects models, hierarchical models or multilevel models) using the R language, the Quantum Forest blog has several recent posts that will be of interest. Written by Luis Apiolaza from the School of Forestry at the University of Canterbury ... [Read more...]

Introduction This semester I’m TA’ing a course on the Psychology of Music taught by Phil Johnson-Laird. It’s been a great course to teach because (i) so much of the material is new to me and (ii) because the study of the psychology of music brings together so ... [Read more...]

Here is our discussion (with Nicolas Chopin) of the Read Paper of last Wednesday by T. van Erven, P. Grünwald and S. de Rooij (Centrum voor Wiskunde en Informatica, Amsterdam), entitled Catching up faster by switching sooner: a predictive approach to adaptive estimation with an application to the Akaike ... [Read more...]

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Recently, I have been reading odd things about strategies to win at the lottery. E.g. or I wrote something a long time ago, but maybe it would be better to write another post. First, it is easy to get data on the French lotteries, including dra... [Read more...]

Fig. 4 – Boxplots of the evolution [against ε] of ABC approximations to the Bayes factor. The representation is made in terms of frequencies of visits to [accepted proposals from] models MA(1) and MA(2) during an ABC simulation when ε corresponds to the 10,1,.1,.01% quantiles on the simulated autocovariance distances. The data is a time [...] [Read more...]

Bill Bolstad's response to Xi'an's review of his book Understanding Computational Bayesian Statistics included the following comment, which I found interesting: Frequentist p-values are constructed in the parameter dimension using a probability distribution defined only in the observation dimension. Bayesian credible intervals are constructed in the parameter dimension using a ... [Read more...]

Bill Bolstad wrote a reply to my review of his book Understanding computational Bayesian statistics last week and here it is, unedited except for the first paragraph where he thanks me for the opportunity to respond, “so readers will see that the book has some good features beyond having a “... [Read more...]

There is a call for postdoctoral positions supported by the Paris Mathematical Sciences Foundation. The deadline is December 13 and the on-line application is available. If you are interested in working with me on Bayesian statistics  (model choice, time series model) or computational methods (SMC, MCMC, ABC, &c.) thru this call, ... [Read more...]