## Archive for book review

## the last argument of kings [jatp]

Posted in Statistics with tags book review, Joe Abercrombie, Paris, Invalides, Canon, jatp, Louis XIV, ultima ratio regum, Richelieu, The Last Argument of Kings on January 21, 2019 by xi'an## Ka [book review]

Posted in Books, pictures, Travel with tags alien intelligence, Anthropocene, book review, crow, Happy New Year, immortality, John Crowley, Locus, raven on January 19, 2019 by xi'an**M**y 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 AIQ, AlphaGo, birthday problem, book review, communist party, computer science, cryptography, Czechoslovakia, error correcting codes, Fred Jelinek, Google, hidden Markov models, James Hellis, John von Neumann, Markov chains, Mersenne twister, obituary, PageRank, Viterbi's algorithm, vulgarisation, word segmentation 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.

## Markov Chains [not a book review]

Posted in Books, pictures, Statistics, University life with tags book review, concentration inequalities, coupling, Eric Moulines, irreducibility, Markov chain and stochastic stability, Markov chain Monte Carlo, Markov chains, MCMC convergence, probability theory, Randal Douc, Richard Tweedie, Sean Meyn, Wasserstein distance on January 14, 2019 by xi'an**A**s Randal Douc and Éric Moulines are both very close friends and two authors of this book on Markov chains, I cannot engage into a regular book review! Judging from the table of contents, the coverage is not too dissimilar to the now classic Markov chain Stochastic Stability book by Sean Meyn and the late Richard Tweedie (1994), called the Bible of Markov chains by Peter Glynn, with more emphasis on convergence matters and a more mathematical perspective. The 757 pages book also includes a massive appendix on maths and probability background. As indicated in the preface, “the reason [the authors] thought it would be useful to write a new book is to survey some of the developments made during the 25 years that have elapsed since the publication of Meyn and Tweedie (1993b).” Connecting with the theoretical developments brought by MCMC methods. Like subgeometric rates of convergence to stationarity, sample paths, limit theorems, and concentration inequalities. The book also reflects on the numerous contributions of the authors to the field. Hence a perfect candidate for teaching Markov chains to mathematically well-prepared. graduate audiences. Congrats to the authors!

## a book and two chapters on mixtures

Posted in Books, Statistics, University life with tags astrostatistics, Bayesian model choice, book review, clustering, CRC Press, Gibbs sampling, handbook of mixture analysis, Handbooks of Modern Statistical Methods, hidden Markov models, MCMC, mixtures of distributions, mixtures of experts, reversible jump MCMC, unknown number of components on January 8, 2019 by xi'an**T**he Handbook of Mixture Analysis is now out! After a few years of planning, contacts, meetings, discussions about notations, interactions with authors, further interactions with late authors, repeating editing towards homogenisation, and a final professional edit last summer, this collection of nineteen chapters involved thirty-five contributors. I am grateful to all participants to this piece of work, especially to Sylvia Früwirth-Schnatter for being a driving force in the project and for achieving a much higher degree of homogeneity in the book than I expected. I would also like to thank Rob Calver and Lara Spieker of CRC Press for their boundless patience through the many missed deadlines and their overall support.

Two chapters which I co-authored are now available as arXived documents:

5. Gilles Celeux, Kaniav Kamary, Gertraud Malsiner-Walli, Jean-Michel Marin, and Christian P. Robert, Computational Solutions for Bayesian Inference in Mixture Models

7. Gilles Celeux, Sylvia Früwirth-Schnatter, and Christian P. Robert, Model Selection for Mixture Models – Perspectives and Strategies

along other chapters

1. Peter Green, Introduction to Finite Mixtures

8. Bettina Grün, Model-based Clustering

12. Isobel Claire Gormley and Sylvia Früwirth-Schnatter, Mixtures of Experts Models

13. Sylvia Kaufmann, Hidden Markov Models in Time Series, with Applications in Economics

14. Elisabeth Gassiat, Mixtures of Nonparametric Components and Hidden Markov Models

19. Michael A. Kuhn and Eric D. Feigelson, Applications in Astronomy