Archive for book review

La peste et la vigne [book review]

Posted in Books, Kids, Travel with tags , , , , , , , on March 17, 2019 by xi'an

During my trip to Cambodia, I read the second volume of this fantasy cycle in French. Which I liked almost as much as the first volume since the author continues to explore the mystery of the central character Syffe and its relations with some magical forces at play in his universe. As in most stories uniquely centred on a single character point of view the recurring ponderings of Syffe about his role in life, the existence of supernatural forces, and his own sanity may tend to get annoying at time. But the escape from the mines and the subsequent stay in a mountain kingdom are well-paced, especially the description of the plague that allows such an escape. The last section is more connected with the first volume and sees more warfare, again with sudden reversals of fortune (no further spoiler!). The final chapters see a lot explained about many aspects of the story and the raison d’être of the character, even though the very last surprise is somewhat predictable. But opening new vistas for the future volumes. There are still many threads I could have pulled to point some potential influences of earlier cycles, from Stephen Donaldson’s Thomas Covenant chronicles, which I simply hated!, to Robin Hobb’s Soldier’s son. Since both stories convey the feeling of a magical force at the level of the whole land (or universe), with the unprepared and imperfect “hero” able to impact this land in dramatic ways. And again Elizabeth Moon’s Deeds of Paksenarion for the depiction of mercenary companies…

sorcerer to the Crown [book review]

Posted in Books, Kids with tags , , , , , , , , , on March 10, 2019 by xi'an

Sorcerer to the Crown is an historical fantasy book by Zen Cho I got into buying by reading a review linking most positively the novel to the monumental Jonathan Strange & Mr. Norrell. Obviously I should have known better, given that Jonathan Strange & Mr. Norrell was several years in the making, with both a very convincing reconstitution of a 19th Century style and a fairly deep plot with fantastic historical connections that took me several reads (and the help of the BBC rendering) to completely understand. Nothing of the sort with this first book in the series, except for the acknowledged influence of Susanna Clarke’s novel. I started reading Sorcerer to the Crown wondering whether this was the young adult version of the other book, the parallel being almost obvious, from the decline of English magic to the Fairy Land accessible from a shrinking number of places, to the inhumanity (or rather a-humanity) of the King of the Fairies, to the old men ruling the magician society by being adverse to any sort of innovation. The attempts at differentiating the story from this illustrious predecessor are somewhat heavy-handed as the author tackles all at once race (the two main characters are African and Indian, respectively, and face discrimination, albeit far from the extent they would have been subjected to in the actual late 1700’s England), gender (magic is repressed in girls from the upper classes), class (see previous!), politics (the British Crown would like very much the help of magicians in fighting Napoléon), imperialism (as British links with India and Malaysia are shown to support local rulers towards gaining hold in these countries).  Once more, Jonathan Strange & Mr. Norrell addresses these issues more subtly from Stephen Black‘s significant role in the story, to the equally major impact of Arabella Strange in the unraveling of her husband greatness, to the contributions of Jonathan Strange to the Napoleonic wars… This however made for a light travel read that I completed within a few days. Enjoying the dialogues more than the [rather uni-dimensional] characters and the low-intensity action scenes.

L’enfant de poussière [book review]

Posted in Books, Kids with tags , , , , , , , , , on February 3, 2019 by xi'an

I read this book in French, as this was the language in which it was written and also because I was given a free copy for writing a review! This is a rather unusual book, the first volume of a series called the cycle of Syffe (where Syffe is both the main character and the name of a tribe), well-written by a young author, although the style is at time a wee bit heavy. As for instance in “Les mains sur les hanches, mes yeux balayèrent l’horizon qui semblait s’étaler de la pointe de mes bottes jusqu’au bout du monde.”

The story in itself borrows to some usual memes of the genre, from following a group of young people (very young in this case), forced into dramatic circumstances by the upheaval of their world, here the death of a king leading to a breakup of his kingdom, and meeting unexpected tutors who will turn them into heroes of sort, if they survive the training. The closest books I can think of are (my favourite) Elizabeth Moon’s Deed of Paksenarrion (without the über-religious aspects [so far!]) and Glen Cook’s Black Company, which both follow mercenary companies in a fragmented world at war. A little bit of Mark Lawrence’s Prince of Thorn as well, since in the later a young kid is driving a band of bandits. And not to forget Joe Abercrombie for the rather similar gritty style. (Gritty enough to make me decide after a few chapters that this was definitely not a young adult novel, as I had doubts about it first.)

The book, first of the cycle, thus follows the misadventures of a very young orphan, and I repeat “very young”, because this is an issue with the story, when 8 to 10 years old are shown in situations and with attitudes that do not sound likely. Even for orphans, even in a medieval world with short lifespans and plenty of economic reasons to turn kids into cheap labour. From spy, to stable boy, to child-soldier. Without turning to spoilers, there are also a bucketful of fortune reversals in the book, meaning that the surroundings and circumstances keep changing, sometimes really fast, sometimes quite slowly, as with the years when Syffe acquires fighting skills from an old mercenary from a tribe of free and deadly fighters. The pace is still good enough for the book to be a page-turner that I read in less than a week! And the few battle scenes are realistic in the Abercrombie referential, that is, with everyone scared and unclear why they are there. There is also some magic involved, which is always a risk in the plot, but apart from a lengthy passage on a malevolent Dream with much too real consequences (nothing to do with Tel’aran’rhiod in the Wheel of Time!), the author handles it quite well, maintaining an ambivalence in Syffe about his super-natural experiences, supported by one of his mentors’ freethinker ethics. As for the completeness of the background, i.e., the universe imagined by the author, it often feels too provincial, too local, with the incoming wars between the local lords sounding very much parochial, although the scope gets gradually wider, along with the maturation of Syffe and the darkening of the overall atmosphere. After finishing the book, I read that seven volumes in total are planned in the cycle!

Computational Bayesian Statistics [book review]

Posted in Books, Statistics with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , on February 1, 2019 by xi'an

This Cambridge University Press book by M. Antónia Amaral Turkman, Carlos Daniel Paulino, and Peter Müller is an enlarged translation of a set of lecture notes in Portuguese. (Warning: I have known Peter Müller from his PhD years in Purdue University and cannot pretend to perfect objectivity. For one thing, Peter once brought me frozen-solid beer: revenge can also be served cold!) Which reminds me of my 1994 French edition of Méthodes de Monte Carlo par chaînes de Markov, considerably upgraded into Monte Carlo Statistical Methods (1998) thanks to the input of George Casella. (Re-warning: As an author of books on the same topic(s), I can even less pretend to objectivity.)

“The “great idea” behind the development of computational Bayesian statistics is the recognition that Bayesian inference can be implemented by way of simulation from the posterior distribution.”

The book is written from a strong, almost militant, subjective Bayesian perspective (as, e.g., when half-Bayesians are mentioned!). Subjective (and militant) as in Dennis Lindley‘s writings, eminently quoted therein. As well as in Tony O’Hagan‘s. Arguing that the sole notion of a Bayesian estimator is the entire posterior distribution. Unless one brings in a loss function. The book also discusses the Bayes factor in a critical manner, which is fine from my perspective.  (Although the ban on improper priors makes its appearance in a very indirect way at the end of the last exercise of the first chapter.)

Somewhat at odds with the subjectivist stance of the previous chapter, the chapter on prior construction only considers non-informative and conjugate priors. Which, while understandable in an introductory book, is a wee bit disappointing. (When mentioning Jeffreys’ prior in multidimensional settings, the authors allude to using univariate Jeffreys’ rules for the marginal prior distributions, which is not a well-defined concept or else Bernardo’s and Berger’s reference priors would not have been considered.) The chapter also mentions the likelihood principle at the end of the last exercise, without a mention of the debate about its derivation by Birnbaum. Or Deborah Mayo’s recent reassessment of the strong likelihood principle. The following chapter is a sequence of illustrations in classical exponential family models, classical in that it is found in many Bayesian textbooks. (Except for the Poison model found in Exercise 3.3!)

Nothing to complain (!) about the introduction of Monte Carlo methods in the next chapter, especially about the notion of inference by Monte Carlo methods. And the illustration by Bayesian design. The chapter also introduces Rao-Blackwellisation [prior to introducing Gibbs sampling!]. And the simplest form of bridge sampling. (Resuscitating the weighted bootstrap of Gelfand and Smith (1990) may not be particularly urgent for an introduction to the topic.) There is furthermore a section on sequential Monte Carlo, including the Kalman filter and particle filters, in the spirit of Pitt and Shephard (1999). This chapter is thus rather ambitious in the amount of material covered with a mere 25 pages. Consensus Monte Carlo is even mentioned in the exercise section.

“This and other aspects that could be criticized should not prevent one from using this [Bayes factor] method in some contexts, with due caution.”

Chapter 5 turns back to inference with model assessment. Using Bayesian p-values for model assessment. (With an harmonic mean spotted in Example 5.1!, with no warning about the risks, except later in 5.3.2.) And model comparison. Presenting the whole collection of xIC information criteria. from AIC to WAIC, including a criticism of DIC. The chapter feels somewhat inconclusive but methinks this is the right feeling on the current state of the methodology for running inference about the model itself.

“Hint: There is a very easy answer.”

Chapter 6 is also a mostly standard introduction to Metropolis-Hastings algorithms and the Gibbs sampler. (The argument given later of a Metropolis-Hastings algorithm with acceptance probability one does not work.) The Gibbs section also mentions demarginalization as a [latent or auxiliary variable] way to simulate from complex distributions [as we do], but without defining the notion. It also references the precursor paper of Tanner and Wong (1987). The chapter further covers slice sampling and Hamiltonian Monte Carlo, the later with sufficient details to lead to reproducible implementations. Followed by another standard section on convergence assessment, returning to the 1990’s feud of single versus multiple chain(s). The exercise section gets much larger than in earlier chapters with several pages dedicated to most problems. Including one on ABC, maybe not very helpful in this context!

“…dimension padding (…) is essentially all that is to be said about the reversible jump. The rest are details.”

The next chapter is (somewhat logically) the follow-up for trans-dimensional problems and marginal likelihood approximations. Including Chib’s (1995) method [with no warning about potential biases], the spike & slab approach of George and McCulloch (1993) that I remember reading in a café at the University of Wyoming!, the somewhat antiquated MC³ of Madigan and York (1995). And then the much more recent array of Bayesian lasso techniques. The trans-dimensional issues are covered by the pseudo-priors of Carlin and Chib (1995) and the reversible jump MCMC approach of Green (1995), the later being much more widely employed in the literature, albeit difficult to tune [and even to comprehensively describe, as shown by the algorithmic representation in the book] and only recommended for a large number of models under comparison. Once again the exercise section is most detailed, with recent entries like the EM-like variable selection algorithm of Ročková and George (2014).

The book also includes a chapter on analytical approximations, which is also the case in ours [with George Casella] despite my reluctance to bring them next to exact (simulation) methods. The central object is the INLA methodology of Rue et al. (2009) [absent from our book for obvious calendar reasons, although Laplace and saddlepoint approximations are found there as well]. With a reasonable amount of details, although stopping short of implementable reproducibility. Variational Bayes also makes an appearance, mostly following the very recent Blei et al. (2017).

The gem and originality of the book are primarily to be found in the final and ninth chapter where four software are described, all with interfaces to R: OpenBUGS, JAGS, BayesX, and Stan, plus R-INLA which is processed in the second half of the chapter (because this is not a simulation method). As in the remainder of the book, the illustrations are related to medical applications. Worth mentioning is the reminder that BUGS came in parallel with Gelfand and Smith (1990) Gibbs sampler rather than as a consequence. Even though the formalisation of the Markov chain Monte Carlo principle by the later helped in boosting the power of this software. (I also appreciated the mention made of Sylvia Richardson’s role in this story.) Since every software is illustrated in depth with relevant code and output, and even with the shortest possible description of its principle and modus vivendi, the chapter is 60 pages long [and missing a comparative conclusion]. Given my total ignorance of the very existence of the BayesX software, I am wondering at the relevance of its inclusion in this description rather than, say, other general R packages developed by authors of books such as Peter Rossi. The chapter also includes a description of CODA, with an R version developed by Martin Plummer [now a Warwick colleague].

In conclusion, this is a high-quality and all-inclusive introduction to Bayesian statistics and its computational aspects. By comparison, I find it much more ambitious and informative than Albert’s. If somehow less pedagogical than the thicker book of Richard McElreath. (The repeated references to Paulino et al.  (2018) in the text do not strike me as particularly useful given that this other book is written in Portuguese. Unless an English translation is in preparation.)

Disclaimer: this book was sent to me by CUP for endorsement and here is what I wrote in reply for a back-cover entry:

An introduction to computational Bayesian statistics cooked to perfection, with the right mix of ingredients, from the spirited defense of the Bayesian approach, to the description of the tools of the Bayesian trade, to a definitely broad and very much up-to-date presentation of Monte Carlo and Laplace approximation methods, to an helpful description of the most common software. And spiced up with critical perspectives on some common practices and an healthy focus on model assessment and model selection. Highly recommended on the menu of Bayesian textbooks!

And this review is likely to appear in CHANCE, in my book reviews column.

the last argument of kings [jatp]

Posted in Statistics with tags , , , , , , , , , on January 21, 2019 by xi'an

Ka [book review]

Posted in Books, pictures, Travel with tags , , , , , , , , on January 19, 2019 by xi'an

My last book of the year (2018), which I finished one hour before midnight, on 31 December! Ka is a book about a crow, or rather, a  Crow, Dar Oakley (or, in full, Dar of the Oak by the Lea), told from his viewpoint, and spanning all of Anthropocene, for Dar Oakley is immortal [sort of] and able to communicate with humans (and other birds, like Ravens. And coyotes). This summary of the plot may sound of limited appeal, but this may be the best book I read this past year. The Washington Post offers a critical entry into Ka that is much better than anything I can state about it. Not only it is about Crows and Ravens, fascinating social birds with a highly developed vocabulary that reflects the hierarchies in these avian societies. But it also offers another view on the doomed history of mankind, to which Crows seem irremediably linked and with whom  Dar Oakley is sharing more that a territory. As so acutely perceived in another review from Locus, the beauty of the book and the genius of the writer, John Crowley, is to translate an alien intelligence in terms intelligible to the reader.

“A crow alone is no crow.”

A fairly, faery, unique, strangely moving, book, thus, that cannot suffer to be labelled into a category like fantasy or poetry or philosophical tale. Reflecting on the solitude brought by knowledge and communicating with another race. And of the bittersweet pain brought by immortality that makes Dar Oakley seek a former mate in the kingdom of dead Crows. An imperfect, fallible character, a perfect messenger of Death to accompany humanity on its last steps.

the beauty of maths in computer science [book review]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , , on January 17, 2019 by xi'an

CRC Press sent me this book for review in CHANCE: Written by Jun Wu, “staff research scientist in Google who invented Google’s Chinese, Japanese, and Korean Web search algorithms”, and translated from the Chinese, 数学之美, originating from Google blog entries. (Meaning most references are pre-2010.) A large part of the book is about word processing and web navigation, which is the author’s research specialty. And not so much about mathematics. (When rereading the first chapters to start this review I then realised why the part about language processing in AIQ sounded familiar: I had read it in the Beauty of Mathematics in Computer Science.)

In the first chapter, about the history of languages, I found out, among other things, that ancient Jewish copists of the Bible had an error correcting algorithm consisting in giving each character a numerical equivalent, summing up each row, then all rows, and  checking the sum at the end of the page was the original one. The second chapter explains why the early attempts at language computer processing, based on grammar rules, were unsuccessful and how a statistical approach had broken the blockade. Explained via Markov chains in the following chapter. Along with the Good-Turing [Bayesian] estimate of the transition probabilities. Next comes a short and low-tech chapter on word segmentation. And then an introduction to hidden Markov models. Mentioning the Baum-Welch algorithm as a special case of EM, which makes a return by Chapter 26. Plus a chapter on entropies and Kullback-Leibler divergence.

A first intermede is provided by a chapter dedicated to the late Frederick Jelinek, the author’s mentor (including what I find a rather unfortunate equivalent drawn between the Nazi and Communist eras in Czechoslovakia, p.64). Chapter that sounds a wee bit too much like an extended obituary.

The next section of chapters is about search engines, with a few pages on Boolean logic, dynamic programming, graph theory, Google’s PageRank and TF-IDF (term frequency/inverse document frequency). Unsurprisingly, given that the entries were originally written for Google’s blog, Google’s tools and concepts keep popping throughout the entire book.

Another intermede about Amit Singhal, the designer of Google’s internal search ranking system, Ascorer. With another unfortunate equivalent with the AK-47 Kalashnikov rifle as “elegantly simple”, “effective, reliable, uncomplicated, and easy to implement or operate” (p.105). Even though I do get the (reason for the) analogy, using an equivalent tool which purpose is not to kill other people would have been just decent…

Then chapters on measuring proximity between news articles by (vectors in a 64,000 dimension vocabulary space and) their angle, and singular value decomposition, and turning URLs as long integers into 16 bytes random numbers by the Mersenne Twister (why random, except for encryption?), missing both the square in von Neumann’s first PRNG (p.124) and the opportunity to link the probability of overlap with the birthday problem (p.129). Followed by another chapter on cryptography, always a favourite in maths vulgarisation books (but with no mention made of the originators of public key cryptography, like James Hellis or the RSA trio, or of the impact of quantum computers on the reliability of these methods). And by an a-mathematic chapter on spam detection.

Another sequence of chapters cover maximum entropy models (in a rather incomprehensible way, I think, see p.159), continued with an interesting argument how Shannon’s first theorem predicts that it should be faster to type Chinese characters than Roman characters. Followed by the Bloom filter, which operates as an approximate Poisson variate. Then Bayesian networks where the “probability of any node is computed by Bayes’ formula” [not really]. With a slightly more advanced discussion on providing the highest posterior probability network. And conditional random fields, where the conditioning is not clearly discussed (p.192). Next are chapters about Viterbi’s algorithm (and successful career) and the EM algorithm, nicknamed “God’s algorithm” in the book (Chapter 26) although I never heard of this nickname previously.

The final two chapters are on neural networks and Big Data, clearly written later than the rest of the book, with the predictable illustration of AlphaGo (but without technical details). The twenty page chapter on Big Data does not contain a larger amount of mathematics, with no equation apart from Chebyshev’s inequality, and a frequency estimate for a conditional probability. But I learned about 23&me running genetic tests at a loss to build a huge (if biased) genetic database. (The bias in “Big Data” issues is actually not covered by this chapter.)

“One of my main objectives for writing the book is to introduce some mathematical knowledge related to the IT industry to people who do not work in the industry.”

To conclude, I found the book a fairly interesting insight on the vision of his field and job experience by a senior scientist at Google, with loads of anecdotes and some historical backgrounds, but very Google-centric and what I felt like an excessive amount of name dropping and of I did, I solved, I &tc. The title is rather misleading in my opinion as the amount of maths is very limited and rarely sufficient to connect with the subject at hand. Although this is quite a relative concept, I did not spot beauty therein but rather technical advances and trick, allowing the author and Google to beat the competition.