Of interest for xkcd fans: What If?: Serious Scientific Answers to Absurd Hypothetical Questions is out! Actually, it is currently the #1 bestseller on amazon! (A physics book makes it to the top of the bestseller list, a few weeks after a theoretical economics book got there. Nice! Actually, a statistics book also made it to the top: Nate Silver’s The SIgnal and the Noise….) I did not read the book, but it is made of some of the questions answered by Randall Munroe (the father of xkcd) on his what if blog. In connection with this publication, Randall Munroe is interviewed on FiveThirtyEight (Nate Silver’s website), as kindly pointed out to me by Bill Jefferys. The main message is trying to give people a feeling about numbers, a rough sense of numeracy. Which was also the purpose of the guesstimation books.
Archive for Amazon
Great poster session yesterday night and at lunch today. Saw an ABC poster (by Dennis Prangle, following our random forest paper) and several MCMC posters (by Marco Banterle, who actually won one of the speed-meeting mini-project awards!, Michael Betancourt, Anne-Marie Lyne, Murray Pollock), and then a rather different poster on Mondrian forests, that generalise random forests to sequential data (by Balaji Lakshminarayanan). The talks all had interesting aspects or glimpses about big data and some of the unnecessary hype about it (them?!), along with exposing the nefarious views of Amazon to become the Earth only seller!, but I particularly enjoyed the astronomy afternoon and even more particularly Steve Roberts sweep through astronomy machine-learning. Steve characterised variational Bayes as picking your choice of sufficient statistics, which made me wonder why there were no stronger connections between variational Bayes and ABC. He also quoted the book The Fourth Paradigm: Data-Intensive Scientific Discovery by Tony Hey as putting forward interesting notions. (A book review for the next vacations?!) And also mentioned zooniverse, a citizens science website I was not aware of. With a Bayesian analysis of the learning curve of those annotating citizens (in the case of supernovae classification). Big deal, indeed!!!
This introduction to Bayesian Analysis, Bayes’ Rule, was written by James Stone from the University of Sheffield, who contacted CHANCE suggesting a review of his book. I thus bought it from amazon to check the contents. And write a review.
First, the format of the book. It is a short paper of 127 pages, plus 40 pages of glossary, appendices, references and index. I eventually found the name of the publisher, Sebtel Press, but for a while thought the book was self-produced. While the LaTeX output is fine and the (Matlab) graphs readable, pictures are not of the best quality and the display editing is minimal in that there are several huge white spaces between pages. Nothing major there, obviously, it simply makes the book look like course notes, but this is in no way detrimental to its potential appeal. (I will not comment on the numerous appearances of Bayes’ alleged portrait in the book.)
“… (on average) the adjusted value θMAP is more accurate than θMLE.” (p.82)
Bayes’ Rule has the interesting feature that, in the very first chapter, after spending a rather long time on Bayes’ formula, it introduces Bayes factors (p.15). With the somewhat confusing choice of calling the prior probabilities of hypotheses marginal probabilities. Even though they are indeed marginal given the joint, marginal is usually reserved for the sample, as in marginal likelihood. Before returning to more (binary) applications of Bayes’ formula for the rest of the chapter. The second chapter is about probability theory, which means here introducing the three axioms of probability and discussing geometric interpretations of those axioms and Bayes’ rule. Chapter 3 moves to the case of discrete random variables with more than two values, i.e. contingency tables, on which the range of probability distributions is (re-)defined and produces a new entry to Bayes’ rule. And to the MAP. Given this pattern, it is not surprising that Chapter 4 does the same for continuous parameters. The parameter of a coin flip. This allows for discussion of uniform and reference priors. Including maximum entropy priors à la Jaynes. And bootstrap samples presented as approximating the posterior distribution under the “fairest prior”. And even two pages on standard loss functions. This chapter is followed by a short chapter dedicated to estimating a normal mean, then another short one on exploring the notion of a continuous joint (Gaussian) density.
“To some people the word Bayesian is like a red rag to a bull.” (p.119)
Bayes’ Rule concludes with a chapter entitled Bayesian wars. A rather surprising choice, given the intended audience. Which is rather bound to confuse this audience… The first part is about probabilistic ways of representing information, leading to subjective probability. The discussion goes on for a few pages to justify the use of priors but I find completely unfair the argument that because Bayes’ rule is a mathematical theorem, it “has been proven to be true”. It is indeed a maths theorem, however that does not imply that any inference based on this theorem is correct! (A surprising parallel is Kadane’s Principles of Uncertainty with its anti-objective final chapter.)
All in all, I remain puzzled after reading Bayes’ Rule. Puzzled by the intended audience, as contrary to other books I recently reviewed, the author does not shy away from mathematical notations and concepts, even though he proceeds quite gently through the basics of probability. Therefore, potential readers need some modicum of mathematical background that some students may miss (although it actually corresponds to what my kids would have learned in high school). It could thus constitute a soft entry to Bayesian concepts, before taking a formal course on Bayesian analysis. Hence doing no harm to the perception of the field.
First day at AISTATS 2014! After three Icelandic vacations days driving (a lot) and hinkg (too little) around South- and West-Iceland, I joined close to 300 attendees for this edition of the AISTATS conference series. I was quite happy to be there, if only because I had missed the conference last year (in Phoenix) and did not want this to become a tradition… Second, the mix of statistics, artificial intelligence and machine learning that characterises this conference is quite exciting, if challenging at time. What I most appreciated in this discovery of the conference is the central importance of the poster session, most talks being actually introductions to or oral presentations of posters! I find this feature terrific enough (is there such a notion as “terrific enough”?!) worth adopting in future conferences I am involved in. I just wish I had managed to tour the whole collection of posters today… The (first and) plenary lecture was delivered by Peter Bühlman, who spoke about a compelling if unusual (for me) version of causal inference. This was followed by sessions on Gaussian processes, graphical models, and mixed data sources. One highlight talk was the one by Marc Deisenroth, who showed impressive robotic fast learning based on Gaussian processes. At the end of this full day, I also attended an Amazon mixer where I learned about Amazon‘s entry on the local market, where it seems the company is getting a better picture of the current and future state of the U.S. economy than governmental services, thanks to a very fine analysis of the sales and entries on Amazon‘s entry. Then it was time to bike “home” on my rental bike, in the setting sun…
Last month, I ordered several books on amazon, taking advantage of my amazon associate gains, and some of them were suggested by amazon algorithms based on my recent history. As I had recently read books involving thieves (like Giant Thief, or Broken Blade and the subsequent books), a lot of titles involved thieves or thievery related names… I picked Den of Thieves mainly for its cover as I did not know the author and the story sounded rather common. When I started reading the book, the story got more and more common, pertaining more to an extended Dungeons & Dragons scenario than to a genuine book! The theme of a bright young thief emerging from the gritty underworld of a close city has been over and over exploited in the fantasy literature, the best (?) example being The lies of Locke Lamora. (Whose third volume, The Republic of Thieves, is in my bag for Reykjavik!) This time, the thief does not appear particularly bright, except at times when he starts philosophy-sing with extremely dangerous enemies!, and the way he eventually overcomes insanely unbalanced odds is just too much. Most characters in the novel are not particularly engaging and way too much caricaturesque from the terribly evil sorcerer cavorting with she-demons to the rigid knight sticking to an idealistic vision of the world where ‘honour” and the code of chivalry is the solution to all problems. It is not even in the slightest sarcastic or tongue-in-cheek as the many novels by David Eddings and the main characters are mostly humourless. I wonder why the book did not get better edited as the weaknesses are very easy to spot! A good example where amazon software failed to make a worthy recommendation!