oceanographers in Les Houches

Þe first internal research workshop of our ERC Synergy project OCEAN is taking place in Les Houches, French Alps, this coming week with 15 researchers gathering for brain-storming on some of the themes at the core of the project, like algorithmic tools for multiple decision-making agents, along with Bayesian uncertainty quantification and Bayesian learning under constraints (scarcity, fairness, privacy). Due to the small size of the workshop (which is perfect for engaging into joint work), it could not be housed by the nearby, iconic, École de Physique des Houches but will take place instead in a local hotel.

On the leisurely side, I hope there will be enough snow left for some lunch-time ski breaks [with no bone fracture à la Adapski!] Or, else, that the running trails nearby will prove manageable.

Arnak Dalalyan at the RSS Journal Webinar

My friend and CREST colleague Arnak Dalalyan will (re)present [online] a Read Paper at the RSS on 31 October with my friends Hani Doss and Alain Durmus as discussants:

‘Theoretical Guarantees for Approximate Sampling and Log-Concave Densities’

Arnak Dalalyan ENSAE Paris, France

Sampling from various kinds of distributions is an issue of paramount importance in statistics since it is often the key ingredient for constructing estimators, test procedures or confidence intervals. In many situations, exact sampling from a given distribution is impossible or computationally expensive and, therefore, one needs to resort to approximate sampling strategies. However, there is no well-developed theory providing meaningful non-asymptotic guarantees for the approximate sampling procedures, especially in high dimensional problems. The paper makes some progress in this direction by considering the problem of sampling from a distribution having a smooth and log-concave density defined on ℝᵖ⁠, for some integer p > 0. We establish non-asymptotic bounds for the error of approximating the target distribution by the distribution obtained by the Langevin Monte Carlo method and its variants. We illustrate the effectiveness of the established guarantees with various experiments. Underlying our analysis are insights from the theory of continuous time diffusion processes, which may be of interest beyond the framework of log-concave densities that are considered in the present work.

statistical modeling with R [book review]

Statistical Modeling with R (A dual frequentist and Bayesian approach for life scientists) is a recent book written by Pablo Inchausti, from Uruguay. In a highly personal and congenial style (witness the preface), with references to (fiction) books that enticed me to buy them. The book was sent to me by the JASA book editor for review and I went through the whole of it during my flight back from Jeddah. [Disclaimer about potential self-plagiarism: this post or a likely edited version of it will eventually appear in JASA. If not CHANCE, for once.]

The very first sentence (after the preface) quotes my late friend Steve Fienberg, which is definitely starting on the right foot. The exposition of the motivations for writing the book is quite convincing, with more emphasis than usual put on the notion and limitations of modeling. The discourse is overall inspirational and contains many relevant remarks and links that make it worth reading it as a whole. While heavily connected with a few R packages like fitdist, fitistrplus, brms (a  front for Stan), glm, glmer, the book is wisely bypassing the perilous reef of recalling R bases. Similarly for the foundations of probability and statistics. While lacking in formal definitions, in my opinion, it reads well enough to somehow compensate for this very lack. I also appreciate the coherent and throughout continuation of the parallel description of Bayesian and non-Bayesian analyses, an attempt that often too often quickly disappear in other books. (As an aside, note that hardly anyone claims to be a frequentist, except maybe Deborah Mayo.) A new model is almost invariably backed by a new dataset, if a few being somewhat inappropriate as in the mammal sleep patterns of Chapter 5. Or in Fig. 6.1.

Given that the main motivation for the book (when compared with references like BDA) is heavily towards the practical implementation of statistical modelling via R packages, it is inevitable that a large fraction of Statistical Modeling with R is spent on the analysis of R outputs, even though it sometimes feels a wee bit too heavy for yours truly.  The R screen-copies are however produced in moderate quantity and size, even though the variations in typography/fonts (at least on my copy?!) may prove confusing. Obviously the high (explosive?) distinction between regression models may eventually prove challenging for the novice reader. The specific issue of prior input (or “defining priors”) is briefly addressed in a non-chapter (p.323), although mentions are made throughout preceding chapters. I note the nice appearance of hierarchical models and experimental designs towards the end, but would have appreciated some discussions on missing topics such as time series, causality, connections with machine learning, non-parametrics, model misspecification. As an aside, I appreciated being reminded about the apocryphal nature of Ockham’s much cited quote “Pluralitas non est ponenda sine necessitate“.

Typo Jeffries found in Fig. 2.1, along with a rather sketchy representation of the history of both frequentist and Bayesian statistics. And Jon Wakefield’s book (with related purpose of presenting both versions of parametric inference) was mistakenly entered as Wakenfield’s in the bibliography file. Some repetitions occur. I do not like the use of the equivalence symbol ≈ for proportionality. And I found two occurrences of the unavoidable “the the” typo (p.174 and p.422). I also had trouble with some sentences like “long-run, hypothetical distribution of parameter estimates known as the sampling distribution” (p.27), “maximum likelihood estimates [being] sufficient” (p.28), “Jeffreys’ (1939) conjugate priors” [which were introduced by Raiffa and Schlaifer] (p.35), “A posteriori tests in frequentist models” (p.130), “exponential families [having] limited practical implications for non-statisticians” (p.190), “choice of priors being correct” (p.339), or calling MCMC sample terms “estimates” (p.42), and issues with some repetitions, missing indices for acronyms, packages, datasets, but did not bemoan the lack homework sections (beyond suggesting new datasets for analysis).

A problematic MCMC entry is found when calibrating the choice of the Metropolis-Hastings proposal towards avoiding negative values “that will generate an error when calculating the log-likelihood” (p.43) since it suggests proposed values should not exceed the support of the posterior (and indicates a poor coding of the log-likelihood!). I also find the motivation for the full conditional decomposition behind the Gibbs sampler (p.47) unnecessarily confusing. (And automatically having a Metropolis-Hastings step within Gibbs as on Fig. 3.9 brings another magnitude of confusion.) The Bayes factor section is very terse. The derivation of the Kullback-Leibler representation (7.3) as an expected log likelihood ratio seems to be missing a reference measure. Of course, seeing a detailed coverage of DIC (Section 7.4) did not suit me either, even though the issue with mixtures was alluded to (with no detail whatsoever). The Nelder presentation of the generalised linear models felt somewhat antiquated, since the addition of the scale factor a(φ) sounds over-parameterized.

But those are minor quibble in relation to a book that should attract curious minds of various background knowledge and expertise in statistics, as well as work nicely to support an enthusiastic teacher of statistical modelling. I thus recommend this book most enthusiastically.

quick(er) calculations [book review]

Upon my request, Oxford University Press sent me this book for review in CHANCE. With the extended title How to add, subtract, multiply, divide, square, and square root more swiftly. This short (173 pages) book is written by Trevor Davis Lipscombe, currently Director of the Catholic University of America Press (which are apparently not suited for his books, since his former Physics of Rugby got published by Nottingham University Press). The concept of the book is to list tricks and shortcuts to handle seemingly tough operations on a list of numbers. Illustrated by short anecdotes mostly related to religion, sports (including the Vatican cricket team!), and history, albeit not necessarily related with the computation at hand and not providing an in-depth coverage of calculation across the ages and the cultures. While the topic is rather dry, as illustrated by the section titles, e.g., “Multiply two numbers that differ by 2, 4, 6, or 20” or “Multiply or divide by 66 or 67, 666 or 667” (!), the exposition is somewhat facilitated by the (classics) culture of the author. (I have to confess I got lost by the date chapter, i.e., finding which day of the week was December 18, 1981, for instance. Especially by the concept of Doomsday which I thought was a special day of the year in the UK. Or in the USA.) Still, while recognising some simple decompositions I also used for additions and subtractions, and acknowledging the validity of the many tricks I had never though of, I wonder at the relevance of learning those dozens of approaches beyond maintaining a particular type of mental agility… Or preparing for party show-time. Especially for the operations that do not enjoy exact solutions, like dividing by √3 or multiplying by π… The book reminded me of a physics professor in Caen, Henri Eyraud, who used to approximate powers and roots faster than it took us to get a slide rule out of our bags! But Guesstimation, which I reviewed several years ago, seemed more far-reaching that Quick(er) calculations, in that I had tried to teach my kids (with limited success) how to reach the right order of magnitude of a quantity, but never insisted [beyond primary school] on quick mental calculations. (The Interlude V chapter connects with this idea.)

[Disclaimer about potential self-plagiarism: this post or an edited version should eventually appear in my Books Review section in CHANCE.]

poems that solve puzzles [book review]

Upon request, I received this book from Oxford University Press for review. Poems that Solve Puzzles is a nice title and its cover is quite to my linking (for once!). The author is Chris Bleakley, Head of the School of Computer Science at UCD.

“This book is for people that know algorithms are important, but have no idea what they are.”

These is the first sentence of the book and hence I am clearly falling outside the intended audience. When I asked OUP for a review copy, I was more thinking in terms of Robert Sedgewick’s Algorithms, whose first edition still sits on my shelves and which I read from first to last page when it appeared [and was part of my wife’s booklist]. This was (and is) indeed a fantastic book to learn how to build and optimise algorithms and I gain a lot from it (despite remaining a poor programmer!).

Back to poems, this one reads much more like an history of computer science for newbies than a deep entry into the “science of algorithms”, with imho too little on the algorithms themselves and their connections with computer languages and too much emphasis on the pomp and circumstances of computer science (like so-and-so got the ACM A.M. Turing Award in 19… and  retired in 19…). Beside the antique algorithms for finding primes, approximating π, and computing the (fast) Fourier transform (incl. John Tukey), the story moves quickly to the difference engine of Charles Babbage and Ada Lovelace, then to Turing’s machine, and artificial intelligence with the first checkers codes, which already included some learning aspects. Some sections on the ENIAC, John von Neumann and Stan Ulam, with the invention of Monte Carlo methods (but no word on MCMC). A bit of complexity theory (P versus NP) and then Internet, Amazon, Google, Facebook, Netflix… Finishing with neural networks (then and now), the unavoidable AlphaGo, and the incoming cryptocurrencies and quantum computers. All this makes for pleasant (if unsurprising) reading and could possibly captivate a young reader for whom computers are more than a gaming console or a more senior reader who so far stayed wary and away of computers. But I would have enjoyed much more a low-tech discussion on the construction, validation and optimisation of algorithms, namely a much soft(ware) version, as it would have made it much more distinct from the existing offer on the history of computer science.

[Disclaimer about potential self-plagiarism: this post or an edited version of it will eventually appear in my Books Review section in CHANCE.]