Archive for book review

Bayesian Essentials with R [book review]

Posted in Books, R, Statistics, University life with tags , , , , , , , on July 28, 2016 by xi'an

[A review of Bayesian Essentials that appeared in Technometrics two weeks ago, with the first author being rechristened Jean-Michael!]

“Overall this book is a very helpful and useful introduction to Bayesian methods of data analysis. I found the use of R, the code in the book, and the companion R package, bayess, to be helpful to those who want to begin using  Bayesian methods in data analysis. One topic that I would like to see added is the use of Bayesian methods in change point problems, a topic that we found useful in a recent article and which could be added to the time series chapter. Overall this is a solid book and well worth considering by its intended audience.”
David E. BOOTH
Kent State University

Extending R

Posted in Books, Kids, R, Statistics with tags , , , , , , , , , , , , , , , , , on July 13, 2016 by xi'an

As I was previously unaware of this book coming up, my surprise and excitement were both extreme when I received it from CRC Press a few weeks ago! John Chambers, one of the fathers of S, precursor of R, had just published a book about extending R. It covers some reflections of the author on programming and the story of R (Parts 2 and 1),  and then focus on object-oriented programming (Part 3) and the interfaces from R to other languages (Part 4). While this is “only” a programming book, and thus not strictly appealing to statisticians, reading one of the original actors’ thoughts on the past, present, and future of R is simply fantastic!!! And John Chambers is definitely not calling to simply start over and build something better, as Ross Ihaka did in this [most read] post a few years ago. (It is also great to see the names of friends appearing at times, like Julie, Luke, and Duncan!)

“I wrote most of the original software for S3 methods, which were useful for their application, in the early 1990s.”

In the (hi)story part, Chambers delves into the details of the evolution of S at Bells Labs, as described in his [first]  “blue book” (which I kept on my shelf until very recently, next to the “white book“!) and of the occurrence of R in the mid-1990s. I find those sections fascinating maybe the more because I am somewhat of a contemporary, having first learned Fortran (and Pascal) in the mid-1980’s, before moving in the early 1990s to C (that I mostly coded as translated Pascal!), S-plus and eventually R, in conjunction with a (forced) migration from Unix to Linux, as my local computer managers abandoned Unix and mainframe in favour of some virtual Windows machines. And as I started running R on laptops with the help of friends more skilled than I (again keeping some of the early R manuals on my shelf until recently). Maybe one of the most surprising things about those reminiscences is that the very first version of R was dated Feb 29, 2000! Not because of Feb 29, 2000 (which, as Chambers points out, is the first use of the third-order correction to the Gregorian calendar, although I would have thought 1600 was the first one), but because I would have thought it appeared earlier, in conjunction with my first Linux laptop, but this memory is alas getting too vague!

As indicated above, the book is mostly about programming, which means in my case that some sections are definitely beyond my reach! For instance, reading “the onus is on the person writing the calling function to avoid using a reference object as the argument to an existing function that expects a named list” is not immediately clear… Nonetheless, most sections are readable [at my level] and enlightening about the mottoes “everything that exists is an object” and “everything that happens is a function” repeated throughout.  (And about my psycho-rigid ways of translating Pascal into every other language!) I obviously learned about new commands and notions, like the difference between

x <- 3


x <<- 3

(but I was disappointed to learn that the number of <‘s was not related with the depth or height of the allocation!) In particular, I found the part about replacement fascinating, explaining how a command like

diag(x)[i] = 3

could modify x directly. (While definitely worth reading, the chapter on R packages could have benefited from more details. But as Chambers points out there are whole books about this.) Overall, I am afraid the book will not improve my (limited) way of programming in R but I definitely recommend it to anyone even moderately skilled in the language.

the Grisha trilogy [book review]

Posted in Books, Kids with tags , , , , , , , , on July 3, 2016 by xi'an

And yet another series [suggested by Amazon] I chose at random after reading the summary… The Grisha trilogy was written by Leigh Bardugo and is told by Alina Starkov, a teenage orphan from the fantasy land of Ravka [sounds like Russia, doesn’t it?!] who suddenly discovers powers she did not suspect when fighting supernatural forces. And embarks on a bleak adventure with her childhood friend to safe their country from dark forces. A rather standard trope for the fantasy literature.. The books read well, in a light sense (or mind candy variety, to borrow from the Three-Toed Sloth blog) if addictive. I went over the first one, Shadow and Bone, within a travel day to München and back. Certainly not a major trilogy. And still, those books attracted massive and enthusiastic reviews (one for each book, from different young readers) in The Guardian! And another one in the NYT, nothing less… The explanation is that what I did not get before starting the trilogy [but started suspecting well into the first volume] this is a young adult (or teenager) series. Or even a children’s book, according to The Guardian! So do not expect any level of subtlety or elaborate plots or clever connections with our own world history. Even the Russian environment is caricaturesque with an annoying flow of kvas and tea and caftans. One character is closely related to Rasputin, the ruling family reminds me of the Romanovs, old and grumpy babushkas pop in now and then, the heroes hunt a firebird, &tc.  And still the addiction operates to some level. [Try at your own risk and give the books to younger readers if it does not work!]

Statistical rethinking [book review]

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , , , on April 6, 2016 by xi'an

Statistical Rethinking: A Bayesian Course with Examples in R and Stan is a new book by Richard McElreath that CRC Press sent me for review in CHANCE. While the book was already discussed on Andrew’s blog three months ago, and [rightly so!] enthusiastically recommended by Rasmus Bååth on Amazon, here are the reasons why I am quite impressed by Statistical Rethinking!

“Make no mistake: you will wreck Prague eventually.” (p.10)

While the book has a lot in common with Bayesian Data Analysis, from being in the same CRC series to adopting a pragmatic and weakly informative approach to Bayesian analysis, to supporting the use of STAN, it also nicely develops its own ecosystem and idiosyncrasies, with a noticeable Jaynesian bent. To start with, I like the highly personal style with clear attempts to make the concepts memorable for students by resorting to external concepts. The best example is the call to the myth of the golem in the first chapter, which McElreath uses as an warning for the use of statistical models (which almost are anagrams to golems!). Golems and models [and robots, another concept invented in Prague!] are man-made devices that strive to accomplish the goal set to them without heeding the consequences of their actions. This first chapter of Statistical Rethinking is setting the ground for the rest of the book and gets quite philosophical (albeit in a readable way!) as a result. In particular, there is a most coherent call against hypothesis testing, which by itself justifies the title of the book. Continue reading


Posted in Books, Statistics, University life with tags , , , , , , , , , , on March 7, 2016 by xi'an

Oxford University Press sent me this book by Phyllis Illari and Frederica Russo, Causality (Philosophical theory meets scientific practice) a little while ago. (The book appeared in 2014.) Unless I asked for it, I cannot remember…

“The problem is whether and how to use information of general causation established in science to ascertain individual responsibility.” (p.38)

As the subtitle indicates, this is a philosophy book, not a statistics book. And not particularly intended for statisticians. Hence, I am not exactly qualified to analyse its contents, and even less to criticise its lack of connection with statistics. But this being a blog post…  I read rather slowly through the book, which exposes a wide range (“a map”, p.8) of approaches and perspectives on the notions of causality, some ways to infer about causality, and the point of doing all this, concluding with a relativistic (and thus eminently philosophical) viewpoint defending a “pluralistic mosaic” or a “causal mosaic” that relates to all existing accounts of causality as they “each do something valuable” (p.258). From a naïve bystander perspective, this sounds like a new avatar of deconstructionism applied to causality.

“Simulations can be very illuminating about various phenomena that are complex and have unexpected effects (…) can be run repeatedly to study a system in different situations to those seen for the real system…” (p.15)

This is not to state that the book is uninteresting, as it provides a wide entry into philosophical attempts at categorising and defining causality, if not into the statistical aspects of the issue. (For instance, the problem whether or not causality can be proven uniquely from a statistical perspective is not mentioned.) Among those interesting points in the early chapters, a section (2.5) about simulation. Which however misses the depth of this earlier book on climate simulations I reviewed while in Monash. Or of the discussions at the interdisciplinary seminar last year in Hanover. I.J. Good’s probabilistic causality is mentioned but hardly detailed. (With the warning remark that one “should not confuse predictability with determinism [and] determinism with causality”, p.82.) Continue reading

the unexpected inheritance of Inspector Chopra [very short book review]

Posted in Books, Travel with tags , , , , , , on March 5, 2016 by xi'an

What can be said about this book other than it involves a detective shadowing a suspect with a baby elephant in tow and climbing an escalator in a modern commercial centre with the said elephant, the suspect being none the wisest?! Not much, really. And none of it positive!

This unexpected inheritance of Inspector Chopra is a rather shallow detective story with one-dimensional characters and not much of a plot.  The only original trait being that it takes place in Mumbai, India. But this book is not much deeper than the Bollywood movies it makes fun of. I am fairly glad I got it for half price in Coventry and read it on the way back home…

expectation-propagation from Les Houches

Posted in Books, Mountains, pictures, Statistics, University life with tags , , , , , , , , , , on February 3, 2016 by xi'an

ridge6As CHANCE book editor, I received the other day from Oxford University Press acts from an École de Physique des Houches on Statistical Physics, Optimisation, Inference, and Message-Passing Algorithms that took place there in September 30 – October 11, 2013.  While it is mostly unrelated with Statistics, and since Igor Caron already reviewed the book a year and more ago, I skimmed through the few chapters connected to my interest, from Devavrat Shah’s chapter on graphical models and belief propagation, to Andrea Montanari‘s denoising and sparse regression, including LASSO, and only read in some detail Manfred Opper’s expectation propagation chapter. This paper made me realise (or re-realise as I had presumably forgotten an earlier explanation!) that expectation propagation can be seen as a sort of variational approximation that produces by a sequence of iterations the distribution within a certain parametric (exponential) family that is the closest to the distribution of interest. By writing the Kullback-Leibler divergence the opposite way from the usual variational approximation, the solution equates the expectation of the natural sufficient statistic under both models… Another interesting aspect of this chapter is the connection with estimating normalising constants. (I noticed a slight typo on p.269 in the final form of the Kullback approximation q() to p().


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