Archive for algorithms

poems that solve puzzles [book review]

Posted in Books, Kids, University life with tags , , , , , , , , , , , , , , , , , , on January 7, 2021 by xi'an

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.]

reading pile for X break

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on December 28, 2018 by xi'an

accelerating MCMC

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , on April 11, 2018 by xi'an

As forecasted a rather long while ago (!), I wrote a short and incomplete survey on some approaches to accelerating MCMC. With the massive help of Victor Elvira (Lille), Nick Tawn (Warwick) and Changye Wu (Dauphine). Survey which current version just got arXived and which has now been accepted by WIREs Computational Statistics. The typology (and even the range of methods) adopted here is certainly mostly arbitrary, with suggestions for different divisions made by a very involved and helpful reviewer. While we achieved a quick conclusion to the review process, suggestions and comments are most welcome! Even if we cannot include every possible suggestion, just like those already made on X validated. (WIREs stands for Wiley Interdisciplinary Reviews and its dozen topics cover several fields, from computational stats to biology, to medicine, to engineering.)

A of A

Posted in Books, Kids, Statistics, Travel, University life with tags , , , , , , , , , , , , , , on November 30, 2017 by xi'an

Next June, at the same time as the ISBA meeting in Edinburgh, which is slowly taking shape, there will be an Analysis of Algorithms (AofA) meeting in Uppsala (Sweden) with Luc Devroye as the plenary Flajolet Award speaker. The full name of the conference is the 29th International Conference on Probabilistic, Combinatorial and Asymptotic Methods for the Analysis of Algorithms. While it is unfortunate the two conferences take place at the same time (and not in the same location), this also provides a continuity of conferences with the following week MCqMC in Rennes and the subsequent week summer school in simulation in Warwick (with Art Owen as the LMS Lecturer).

About our summer school, I want to point out that, thanks to several sponsors, we will be able to provide a consequent number of bursaries for junior researchers. This should be an additional incentive for attendees of the previous week Young Bayesian meeting (BAYSM) to remain the extra days nearby Warwick and attend this fantastic opportunity. Other instructors are Nicolas Chopin, Mark Huber and Jeff Rosenthal!

weapons of math destruction [book review]

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , on December 15, 2016 by xi'an

wmd As I had read many comments and reviews about this book, including one by Arthur Charpentier, on Freakonometrics, I eventually decided to buy it from my Amazon Associate savings (!). With a strong a priori bias, I am afraid, gathered from reading some excerpts, comments, and the overall advertising about it. And also because the book reminded me of another quantic swan. Not to mention the title. After reading it, I am afraid I cannot tell my ascertainment has changed much.

“Models are opinions embedded in mathematics.” (p.21)

The core message of this book is that the use of algorithms and AI methods to evaluate and rank people is unsatisfactory and unfair. From predicting recidivism to fire high school teachers, from rejecting loan applications to enticing the most challenged categories to enlist for for-profit colleges. Which is indeed unsatisfactory and unfair. Just like using the h index and citation ranking for promotion or hiring. (The book mentions the controversial hiring of many adjunct faculty by KAU to boost its ranking.) But this conclusion is not enough of an argument to write a whole book. Or even to blame mathematics for the unfairness: as far as I can tell, mathematics has nothing to do with unfairness. Some analysts crunch numbers, produce a score, and then managers make poor decisions. The use of mathematics throughout the book is thus completely inappropriate, when the author means statistics, machine learning, data mining, predictive algorithms, neural networks, &tc. (OK, there is a small section on Operations Research on p.127, but I figure deep learning can bypass the maths.) Continue reading