bayess’ back! [on CRAN]

Introduction to Sequential Monte Carlo [book review]

[Warning: Due to many CoI, from Nicolas being a former PhD student of mine, to his being a current colleague at CREST, to Omiros being co-deputy-editor for Biometrika, this review will not be part of my CHANCE book reviews.]

My friends Nicolas Chopin and Omiros Papaspiliopoulos wrote in 2020 An Introduction to Sequential Monte Carlo (Springer) that took several years to achieve and which I find remarkably coherent in its unified presentation. Particles filters and more broadly sequential Monte Carlo have expended considerably in the last 25 years and I find it difficult to keep track of the main advances given the expansive and heterogeneous literature. The book is also quite careful in its mathematical treatment of the concepts and, while the Feynman-Kac formalism is somewhat scary, it provides a careful introduction to the sampling techniques relating to state-space models and to their asymptotic validation. As an introduction it does not go to the same depths as Pierre Del Moral’s 2004 book or our 2005 book (Cappé et al.). But it also proposes a unified treatment of the most recent developments, including SMC² and ABC-SMC. There is even a chapter on sequential quasi-Monte Carlo, naturally connected to Mathieu Gerber’s and Nicolas Chopin’s 2015 Read Paper. Another significant feature is the articulation of the practical part around a massive Python package called particles [what else?!]. While the book is intended as a textbook, and has been used as such at ENSAE and in other places, there are only a few exercises per chapter and they are not necessarily manageable (as Exercise 7.1, the unique exercise for the very short Chapter 7.) The style is highly pedagogical, take for instance Chapter 10 on the various particle filters, with a detailed and separate analysis of the input, algorithm, and output of each of these. Examples are only strategically used when comparing methods or illustrating convergence. While the MCMC chapter (Chapter 15) is surprisingly small, it is actually an introducing of the massive chapter on particle MCMC (and a teaser for an incoming Papaspiloulos, Roberts and Tweedie, a slow-cooking dish that has now been baking for quite a while!).

Journal of Open Source Software

A week ago, I received a request for refereeing a paper for the Journal of Open Source Software, which I have never seen (or heard of) before. The concept is quite interesting with a scope much broader than statistical computing (as I do not know anyone in the board and no-one there seems affiliated with a Statistics department). Papers are very terse, describing the associated code in one page or two, and the purpose of refereeing is to check the code. (I was asked to evaluate an MCMC R package but declined for lack of time.) Which is a pretty light task if the code is friendly enough to operate right away and provide demos. Best of luck to this endeavour!

Of hunter-gatherers and R packagers

After an exhausting day spent in the train to escort my daughter back from Petite Bretagne, I came home to read about the on-going action of the North Sea fishermen, who blockaded the North Sea ports protesting against the EU fishing quotas.

I usually find the train a great environment to work and this was true on the morning trip where I spent three hours building the R package for our new MCMC book with George Casella. [But on the way back, there were noisy people all over the place and concentrating was a problem…] It took me two days to understand the structure of writing R packages, first by mimicking the LearnBayes package of Jim Albert, then by reading the on-line available documentation. Once I got over the error messages than seemed to imply I did not have the right version of R and once installed the additional codetools package, due to Luke Tierney, I managed to run

R CMD check mcsm

R CMD build mcsm

R CMD INSTALL mcsm

satisfactorily, including the documentation (the worst part!)… I have done the first four chapters so far and the remaining chapters should follow rather quickly. This is quite comforting because this is the very last step of writing the draft of Enter Monte Carlo Statistical Methods (this is the current title, by the way).

PS-Getting back to those fishermen, I quite understand their plight, i.e. that the current quotas are pushing them out of business, but the answer from the French government, namely to sponsor them for not fishing rather than for changing jobs, is absurd. There is enough evidence to support the thesis of a depletion of the fish population in the North Sea and the Atlantic to understand that the culture of hunting-gathering that still underlies commercial fishing is not sustainable. Some species like the tunas are already close to extinction if nothing short of a ban is enforced. This is obviously tough on tuna hunter-gatherers, but they must be stopped…