I am always amazed (and obviously envious) of the facilities offered to both students and faculty living in the colleges (not mentioning of course the rich intellectual atmosphere both in colleges and at the faculty of mathematics, expressed e.g. by an astounding list of seminars). My seminar on ABC model choice was well-attended and discussed, despite being at 4pm a Friday afternoon (maybe the beer social in the faculty upstairs right after helped!). And I had time to work quietly on multiple-try MCMC, following thoughts raised both by the comments on the related post and the GPU meeting in Warwick. Add great weather and the opportunity to go for a run next to the rowers in the morning to make for a great (short) trip.

fAR1=function(n){
 u=runif(n)
 x=rexp(n)
 f=(C*(x)*exp(-2*x^2/3))
 g=dexp(n,1)
 test=(u<f/(3*g))
 y=x[test]
 p=length(y)/n #acceptance probability
 M=1/p
 C=M/3
 hist(y,20,freq=FALSE)
 return(x)
 }

which I find remarkable if alas doomed to fail! I wonder if there exists a (real as opposed to fantasy) computer language where you could introduce constants C and only define them later… (What’s rather sad is that I keep insisting on the fact that accept-reject does not need the constant C to operate. And that I found the same mistake in several of the students’ code. There is a further mistake in the above code when defining g. I also wonder where the 3 came from…)

As Rob J. Hyndman enthusiastically declares in his blog, “this is a gem of a book”. I would go even further and argue that The Art of R programming is a whole mine of gems. The book is well constructed, and has a very coherent structure.

After an introductory chapter, where the reader gets a quick overview on R basics that allows her to work through the examples in the following chapters, the rest of the book can be divided in three main parts. In the first part (Chapters 2 to 6) the reader is introduced to main R objects and to the functions built to handle and operate on each of them. The second part (Chapters 7 to 13) is focussed on general programming issues: R structures and object-oriented nature, I/O, string handling and manipulating issues, and graphics. Chapter 13 is all devoted to the topic of debugging. The third part deals with more advanced topics, such as speed of execution and performance issues (Chapter 14), mix-matching functions written in R and C (or Python), and parallel processing with R. Even though this last part is intended for more experienced programmers, the overall programming skills of the intended reader “may range anywhere from those of a professional software developer to `I took a programming course in college’.” (p.xxii).

With a fluent style, Matloff is able to deal with a large number of topics in a relatively limited number of pages, resulting in an astonishingly complete yet handy guide. At almost every page we discover a new command, most likely the command we had always looked for and done without by means of more or less cumbersome roundabouts. As a matter of fact, it is possible that there exists a ready-made and perfectly suited R function for nearly anything that comes up to one’s mind. Users coming from compiled programming languages may find it difficult to get used to this wealth of functions, just as they may feel uncomfortable not declaring variable types, not initializing vectors and arrays, or getting rid of loops. Nevertheless, through numerous examples and a precise knowledge of its strengths and limitations, Matloff masterly introduces the reader to the flexibility of R. He repeatedly underlines the functional nature of R in every part of the book and stresses from the outset how this feature has to be exploited for an effective programming.

“One of the most effective ways to achieve speed in R code is to use operations that are {\em vectorized}, meaning that a function applied to a vector is actually applied individually to each element.” (p.40). 

The result is so convincing that it pushes even the strictest code purist to free herself from prejudices and surrender to the  pleasures of an interpreted language. This probably was the hardest challenge in writing The Art of R programming, and the author brilliantly met it.

The climax is unquestionably attained in the final chapters, where Matloff introduces some advanced and unusual topics with remarkable clarity and briskness. Within a few pages, he manages to tackle the object-oriented side of R, to advise and instruct the reader on debugging and performance issues, to show how to deal with R and C (or Python) mixed codes, and finally to open new perspectives by presenting the different approaches to parallel R. There is even a mention of GPU programming, a short paragraph certainly inexhaustive, but still instructive. To my knowledge, this is the only R handbook in which parallel programming with R is tackled with some degree of detail (I only found a hint of it in R in a nutshell, yet no programming details are given therein.). Also, the importance and prominence given to debugging are commendable, since this topic is often and mistakenly disregarded in most programming handbooks—except those explicitly written on the subject. Among the sharpest passages of the book, I definitely include the ones on scope and environment issues, to which are devoted both a long section in Chapter 17 and a tiny simple yet enlightening example as early as page 9.

“Note carefully the role of w. The R interpreter found that there was no local variable of that name, so it ascended to the next level [...] where it found a variable w with value 12. [...]. It is possible (though not desirable) to deliberately allow name conflicts in this hierarchy. [...] In such a situation the innermost environment is used first.” (p.153).

The message is clear: know exactly what you want to implement, keep track of all your objects, and scoping will not be an issue but another tool.

“In C, we would not have functions defined within functions [...]. Yet, since functions are objects, it is possible–and sometimes desirable from the point of view of the encapsulation goal of object-oriented programming—to define a function within a function; we are simply creating an object, which we can do anywhere.” (p.152-3).

Another little gem is Section 7.9 on recursion, a concept that Matloff presents in a very clear and intuitive way. This section ends with one the most inspired extended examples proposed in the book, where recursion is used to implement a binary search tree. Other interesting extended examples are those about discrete-event simulation (Section 7.8.3), Markov Chains (Section 8.4.2) and polynomial regression (Section 9.1.7), though these applications may be a little too challenging for readers lacking a solid background in Statistics.

Although The Art of R programming is a book of many virtues, there are in my opinion some flaws:

The presence of lines of R code starting from the first few pages encourages the user to test her understandings straight away while reading, making The Art of R programming a sort of plug-and-play guide through R. Unfortunately, the pleasure of real-time testing is spoiled by two things. First, the reader has to copy those codes line by line. This is unquestionably useful for the many simple examples scattered throughout the book. However, it may become an inexhaustible source of typos, both pointless and annoying—not to mention time-consuming—when it comes to more complicated programs like those expounded in the many Extended Example sections. Second, the databases are unavailable so some applications are simply unusable (I managed to find the abalone data set for extended examples of Sections 2.9.2 and 4.4.3 thus discovering this interesting repository, but for the rest my research was rather inconclusive.) I am referring here to virtually all the extended examples in Chapters 5 and 6 on data frames, factors and tables. In particular, I find the application on the aids for learning Chinese dialect (Section 5.4.3) so over-elaborate to be nearly worthless. I would certainly suggest designing a dedicated package assembling all the necessary material for a fully profitable training with the book, like the package mcsm conceived by Robert and Casella for reproducing the results contained in their book on Monte Carlo methods with R.

In addition, surely R can handle huge databases with great ease, and maybe I am giving way to my personal preferences here, but I find that two whole chapters on data frames and factors (adding up to almost 40 pages!) are perhaps too much. On the contrary, I believe that the “traditional” graphic package would have deserved more space and consideration, not only in the devoted chapter (Chapter 12) but generally throughout the book. Indeed, the author suggests some good handbooks on the subject by Murrel and Wickham, but these are too detailed and advanced to be used for general purposes.

Despite an overall concise style, there are some long-winded passage and repetitions, especially in the applications, where certain lines of code are definitely redundant. I was likewise puzzled by the total absence in the book of the command separator ;, which would have considerably shortened and lightened some unnecessarily long examples. Also, a separate and more detailed index of R commands and functions would be helpful.

Finally, a minor but curious point about the assignment operator. I find the issue of <- vs. = particularly fascinating and a bit perturbing, since this leaves in fact an ambiguity in the definition of such a fundamental operator. Still, there seem to be two main streams and no general agreement. Reading on various blogs and discussion forums, I found no decisive nor robust argument in favor of either. Matloff approaches the issue of <- vs. = in assignments as soon as page 4. As he says, “The standard assignment operator in R is <-. You can also use =, but this is discouraged, as it does not work in some special situations.”. I was really eager to see these “special situations” shown in concrete examples. Unfortunately, they are nowhere to be listed in the book.

Notwithstanding these minor defaults, The Art of R programming is enriching, enjoyable and definitely worthwhile keeping as a reference while working with R. I highly recommend it to programmers, academic researchers and students in computational statistics willing to be quickly operational in writing R software.  And it is undoubtedly a really useful reading for any R user.

I

 1. I was trying to read this book in details on importance sampling. It wasted me a few hours looking at the detailed mathematically formula in the corresponding section in the book without getting a clear high level picture. The convoluted examples given the section is more than necessary. Eventually, I found this series of video lecture from mathematicalmonk on youtube.

If you compare the book with this series of video, I believe you will agree this book diverse a 2 star. Technically this book may be sophisticated. But just by sampling the important sampling section and checking a few other sections in the book, I think that I can conclude fairly safely that if there is anything that a reader don’t understand in the book, it is the author’s fault but not the readers.

and

2. This review is about the material quality of the printing in the copy I received. This is not about the content.

I have access to a real copy of this edition in the local library. It is the usual high quality hardcover: it has a matte cover with texture, beautifully bound; the paper inside is high-quality, very soft and slightly off-white; and the printing of the text is very sharp. The version I received from Amazon claimed to be exactly the same, but was very different:
– The hardcover was shiny, did not have texture, and had a natural tendency to bend strongly outwards, it even cannot stay opened if I leave it alone, it will close.
– The paper inside is whiter, horribly white, like standard printing A4 paper;
– The text printing looks like a cheap photocopy of the original. It don’t even match a home laser printer. Some formulas are difficult to read. Moreover, some pages are not even centered.

It looks and feels like a cheap knock-off photocopy done in a garage. When I pay a lot of money for a hardcover edition I want the real thing, not a cheap knock-off. Authors should avoid their work being degraded with this cheap printing.

and I thought the ‘Og readers might be interested! The second reviewer’s complaint may be about a scam my friend Julien also fall victim to, people pretending to sell the original and making cheap copies. The reviewer should have asked for a refund or else should have returned the book, that’s all.  Nothing us authors can do anything about. Now it may also be a case of poor print-on-demand output from the publisher itself. I have enquired with Springer to see if this may be the case.

The first review from “academic book reviewer” is much more hilarious. And not only for the grammar. The on-line course by mathematicalmonk is a nice explanation I would also recommend to students. However, this on-line course uses about the same arguments as ours and, at some point, the reader (of a graduate mathematical text) needs to get to the foundations of the method(s) and this requires some advanced mathematics. This is missed by a reader who is apparently not much of an academic [reviewer]. (Most of his/her reviews are of the same whining nature.) Still, I love the above line “if there is anything that a reader don’t (sic) understand in the book, it is the author’s fault but not the readers“! I will certainly keep that in mind for future book reviews.

Consider an (n,n) matrix containing the integers from 1 to n². The matrix is “friendly” if the set of the sums of the rows is equal to the set of the sum of the columns. Find examples for n=4,5,6. Why is there no friendly matrix when n=9?

Checking for small n’s seems easy enough:

friend=function(n){
s=1
while (s>0){
  A=matrix(sample(1:n^2),ncol=n)
  s=sum(abs(sort(apply(A,1,sum))-
        sort(apply(A,2,sum))))}
A
}

For instance, running

> friend(3)
     [,1] [,2] [,3]
[1,]    8    4    2
[2,]    1    9    5
[3,]    6    3    7
> friend(4)
     [,1] [,2] [,3] [,4]
[1,]   14   10   11    6
[2,]   13    3   12    8
[3,]    4    5   16   15
[4,]    9    1    2    7
> friend(5)
     [,1] [,2] [,3] [,4] [,5]
[1,]   17   14   10   16    5
[2,]    8   19    7   15   22
[3,]    2    3   25   13    6
[4,]   11   12   18    1   21
[5,]   24   23   20    4    9
>friend(6)
     [,1] [,2] [,3] [,4] [,5] [,6]
[1,]   14    4   36   30   10   18
[2,]    7   16   24   27   32   11
[3,]   21   25   12    5   22   17
[4,]   26   20    6   31   19   34
[5,]    3   35    1   28    9   29
[6,]   23    2   33   15   13    8

produces right answers. But the case n=6 proved itself almost too hard for brute-force handling!!! I have no time to devise a simulated-annealing code to speed up the resolution so this will have to wait till the next weekend edition of Le Monde. As to why n=9 does not enjoy a solution… (When n=2 there is no solution, as proven by the brute force exhibition of all cases.)

T

Some hae meat and canna eat,
And some wad eat that want it;
But we hae meat, and we can eat,
And sae let the Lord be thankit.

and I did manage to go over the book by Gouriéroux and Monfort on indirect inference over the weekend. I still need to beef up the slides before the course starts next Thursday! (The core version of the slides is actually from the course I gave in Wharton more than a year ago.)

A

- Je préfére complet blanc, complet noir. Quand tu te regardes tout nu dans le miroir le matin, t’as pas de problème!

…

- Là-bas, y mange avec les macaques, y mange avec les rats, y nage avec les morts, y vit,  y tombe malade, c’est pas comme ici… Y peut pas tenir longtemps, y meurt au bout du compte, y peut pas fonder une famille, c´est pour cela que j’ai laissé tomber la mère…, c’est pas possible!

…

- Ici, c’est la Sibérie, essaie de dormir dehors, là, dans la voiture, tu peux pas dormir, tu deviens dingue. Y faut s’habituer, quand tu as l’habitude…., c’est plus facile.

T

Then, tomorrow, I am off to Cambridge to talk about ABC and model choice on Friday afternoon. (Presumably using the same slides as in Provo.)

The (1) in the title is in prevision of a second trip to Oxford next month and another one to Bristol two months after! (The trip to Edinburgh does not count of course, since it is in Scotland!)

“…there is almost invariably a peculiar pair of caveats presented as from on high: Never accept the alternative hypothesis, and ever say the probability is 0.95 that the mean lies in a 95% confidence interval for the mean.” Meg Dillon, After Math

Both blogs managed to bemuse me (this is not going to be a very coherent post, I am afraid!): the first one because it has this condescending tone of pure mathematicians about statistics or at least statistics course (i.e. “anyone can teach statistics!” mixed with “I hate teaching statistics!”) that I meet too often, esp. this side of the pond. Plus it seemed to miss the fundamental distinction between probability and statistics (check the above quote). And it did not say why the contents of the French course was much nicer than the equivalent designed by Meg Dillon at her university (except for the fact that she could use measure theory from the start). Maybe the French idiosyncrasy the author basks in is the fact that statistics is not recognised as a field in French universities (there is no stat department for instance) but is instead a subfield of mathematics…

“…stat is a different intellectual discipline.  She longs for a so-called stat course based on sigma-algebras and probability spaces.  Well, that has been tried many times over many years, and it fails miserably at helping students understand the important stat concepts.” Douglas Andrews, ASA Blog Viewer

The second post is making sense in stressing that stat is not math. (Or rather, as it should have been stated, it is not only math.) And that (non-statistician) mathematicians should get some preliminary training or exposure to real data when teaching statistics courses. I can certainly remember a few of my (French) stat teachers who had never approached data in their whole life! However, the comment that “foundation of stat is in empirical science and in learning from observed data, not in math” seems to go overboard. As it echoes in negative the complaint from the math teacher that intro statistics courses were “a hodgepodge of recipes” with no mathematical backbone. My feeling there is that, while we certainly do not need measure theory for the earliest statistics courses (Riemann integration is good enough for my second and third year students), we have to anchor statistical techniques into a mathematical bed to avoid them looking as a bag of tricks. I remember after my first (mathematical) statistics course on being puzzled by the lack of direction and/or the multiplicity, when compared with a standard math course. I was missing the decision-theoretic part that was to come later! Had I been exposed to a non-mathematical intro stat course, I do not think I would have persevered in this field! (And I would have moved to differential geometry instead…)

T

Even though I did not find Tea-Bag perfect in terms of its story, in the sense that it is more allegorical than realistic, esp. with regards to the unlikely involvement of the Swedish writer into the women writing class, and the subsequent involuntary “rescue” of those women, it told the story of three (legal and illegal) migrant women in a highly personal and convincing manner. The different writing styles of those women seems [to me] well-rendered in the book. The part about Tania, the migrant from Russia (or Latvia?), reminded me very vividly of Pudhishtus, or Purge, I reviewed last year, about a young woman fleeing a prostitution ring and arriving at her grand-mother’s farm re-awaking a dreary past for the latter. (I found Purge deeper than Tea-Bag in that much more than the awful plight of the young woman was at stake. But Tea-Bag is, in a way, more optimistic about human nature than Purge…) I quite liked the ending of the book, with the demonstration that good will alone (of the writer) is not going to change (for the better) the situation of those migrant women and that their future is still to be drawn… Not a book with an obvious message, then, more of bearing witness to the hard facts. With a long-lasting impact. Recommended.

“…the argument is false that because some ideal form of this approach to reasoning seems excellent n theory it therefore follows that in practice using this and only this approach to reasoning is the right thing to do.” Stephen Senn, 2011

Deborah Mayo, Aris Spanos, and Kent Staley have edited a special issue of Rationality, Markets and Morals (RMM) (a rather weird combination, esp. for a journal name!) on “Statistical Science and Philosophy of Science: Where Do (Should) They Meet in 2011 and Beyond?” for which comments are open. Stephen Senn has a paper therein entitled You May Believe You Are a Bayesian But You Are Probably Wrong in his usual witty, entertaining, and… Bayesian-bashing style! I find it very kind of him to allow us to remain in the wrong, very kind indeed…

Now, the paper somehow intersects with the comments Stephen made on our review of Harold Jeffreys’ Theory of Probability a while ago. It contains a nice introduction to the four great systems of statistical inference, embodied by de Finetti, Fisher, Jeffreys, and Neyman plus Pearson. The main criticism of Bayesianism à la de Finetti is that it is so perfect as to be outworldish. And, since this perfection is lost in the practical implementation, there is no compelling reason to be a Bayesian. Worse, that all practical Bayesian implementations conflict with Bayesian principles. Hence a Bayesian author “in practice is wrong”. Stephen concludes with a call for eclecticism, quite in line with his usual style since this is likely to antagonise everyone. (I wonder whether or not having no final dot to the paper has a philosophical meaning. Since I have been caught in over-interpreting book covers, I will not say more!) As I will try to explain below, I believe Stephen has paradoxically himself fallen victim of over-theorising/philosophising! (Referring the interested reader to the above post as well as to my comments on Don Fraser’s “Is Bayes posterior quick and dirty confidence?” for more related points. Esp. about Senn’s criticisms of objective Bayes on page 52 that are not so central to this discussion… Same thing for the different notions of probability [p.49] and the relative difficulties of the terms in (2) [p.50]. Deborah Mayo has a ‘deconstructed” version of Stephen’s paper on her blog, with a much deeper if deBayesian philosophical discussion. And then Andrew Jaffe wrote a post in reply to Stephen’s paper. Whose points I cannot discuss for lack of time, but with an interesting mention of Jaynes as missing in Senn’s pantheon.)

“The Bayesian theory is a theory on how to remain perfect but it does not explain how to become good.” Stephen Senn, 2011

While associating theories with characters is a reasonable rethoretical device, especially with large scale characters as the one above!, I think it deters the reader from a philosophical questioning on the theory behind the (big) man. (In fact, it is a form of bullying or, more politely (?), of having big names shoved down your throat as a form of argument.)  In particular, Stephen freezes the (Bayesian reasoning about the) Bayesian paradigm in its de Finetti phase-state, arguing about what de Finetti thought and believed. While this is historically interesting, I do not see why we should care at the praxis level. (I have made similar comments on this blog about the unpleasant aspects of being associated with one character, esp. the mysterious Reverent Bayes!) But this is not my main point.

“…in practice things are not so simple.” Stephen Senn, 2011

The core argument in Senn’s diatribe is that reality is always more complex than the theory allows for and thus that a Bayesian has to compromise on her/his perfect theory with reality/practice in order to reach decisions. A kind of philosophical equivalent to Achille and the tortoise. However, it seems to me that the very fact that the Bayesian paradigm is a learning principle implies that imprecisions and imperfections are naturally endowed into the decision process. Thus avoiding the apparent infinite regress (Regress ins Unendliche) of having to run a Bayesian analysis to derive the prior for the Bayesian analysis at the level below (which is how I interpret Stephen’s first paragraph in Section 3). By refusing the transformation of a perfect albeit ideal Bayesian into a practical if imperfect bayesian (or coherent learner or whatever name that does not sound like being a member of a sect!), Stephen falls short of incorporating the contrainte de réalité into his own paradigm. The further criticisms found about prior justification, construction, evaluation (pp.59-60) are also of that kind, namely preventing the statistician to incorporate a degree of (probabilistic) uncertainty into her/his analysis.

In conclusion, reading Stephen’s piece was a pleasant and thought-provoking moment. I am glad to be allowed to believe I am a Bayesian, even though I do not believe it is a belief! The praxis of thousands of scientists using Bayesian tools with their personal degree of subjective involvement is an evolutive organism that reaches much further than the highly stylised construct of de Finetti (or of de Finetti restaged by Stephen!). And appropriately getting away from claims to being perfect or right. Or even being more philosophical.

O

We are seeking accomplished applicants for a post-doctoral research associate position. Candidates must hold a PhD in mathematical or computational statistics, and the ideal candidate will have research experience in theoretical properties of Bayesian nonparametric statistics, MCMC or SMC algorithms or nonparametric estimation. The associate will work with several members of the BANDHITS (Bayesian nonparametrics, high dimensional techniques and simulation) research project which includes statisticians from several universities  in Paris (Paris-Dauphine,  CREST-ENSAE, Paris 6, Paris 7, University Paris-Sud (Orsay), and CEA). The associate will be expected to participate in research leading to publications in top journals. The appointment, which can begin immediately, will be for a one-year contract. Applicants should email their vita, a brief statement of their background and interests to Judith Rousseau, rousseau[chez]ceremade[dot]dauphine[dot]fr

p=10
T=260
dat=seri=rnorm(T) #white noise

par(mfrow=c(2,2),mar=c(2,2,1,1))
for (i in 1:4){
  coef=runif(p,min=-.5,max=.5)
  for (t in ((p+1):T))
    seri[t]=sum(coef*seri[(t-p):(t-1)])+dat[t]
  plot(seri,ty="l",lwd=2,ylab="")
  }

leading to outputs like the following one

“The thesis of this monograph is that societies in general are governed by objective laws that have their roots in human nature. The task of the social scientist is to discover and explore those laws (…) Null hypotheses and alternative rival hypotheses developed by social scientists must eclectically correlated to mathematical formulae or the laws of physics in order to advance non-speculative, unbiased knowledge.” V.J. Belfiglio (p.x)

So the thesis advanced in Correlations Between the Physical and Social Sciences by Valentine Belfiglio is that social problems can be represented in terms of physical laws. The 41 pages book pushes this argument through four cases studies.

“The first case study relates marital assimilation of minority groups into dominate core cultures with Graham’s Law for the diffusion of gases. The second case study relates the mutual hostility of political leaders with the Mirror Equation employed in basic geometric optics. The third case study relates the duration of major American military conflicts to the formulae for empirical and subjective probabilities. The fourth case study relates the radioactive decay formula for radioactive substances to the rate of decline of several extinct empires” V.J. Belfiglio (p.xi)

As the author himself recognises, “the four case studies in this monograph do not provide definitive answers.” My opinion is that they do not provide answers at all! Indeed, the first chapter contains two 2×2 tables about the endogamous preferences of Mexican and Italian inhabitants of Dallas, Texas. A chi-square test concludes that Mexicans prefer endogamy and that Italians do not. Although Graham’s Law is re-expressed there as “marital assimilation being inversely proportional to the square root of the population densities” (p.3), there is no result based on the data supporting this law. The second chapter is trying to “explore the mutuality of hostility between the Bush and Ahmandinejad (sic) administrations. Spearman’s Rho correlation coefficient” (p.11) is used and found to demonstrate “a perfect positive correlation” (p.12), although the data is quantitative (intensity of hostility between 1 and 9) and not paired. (The study simply shows that the empirical cdfs of the hostility values for both sides are approximately the same, Spearman’s rho test being inappropriate there.) The connection with optics is at best tenuous. Chapter 3 centres on a table for the durations of major American (meaning US) military conflicts. A mere observation is that the US “has been engaged in major wars 56.5 percent of the time between 1775-2010.” (p.24) but Valentine Belfiglio turns this into “empirical probability” (i.e the frequency of wars), a “subjective probability” (i.e. the average number of years of peace between wars), and the “number of possible interaction channels” (i.e. a combination number) as a way to link American foreign policy with probability theory. Again, the connection is non-existent. The fourth and final chapter is about the “correlation between the decay of radioactive substances and the rate of decline of empires.” (p.31) The data is made of the duration of seven empires, associated with estimates of their half-life. The paper concludes on “a perfect negative correlation between the half-lives of empires and their rates of decline” (p.35), which is not very surprising when considering that one is a monotonic function of the other…

“I conclude with the words of Henry Wadsworth Longfellow: “Sometimes we may learn more from a man’s errors, than from his virtues”.” V.J. Belfiglio (p.40)

There is therefore not much to discuss about this book: it does not go beyond stating the obvious, while the connection between the observed social phenomena and generic physical laws remains at the level of a literary ellipse, not of a scientific demonstration. I am deeply puzzled at why a publisher would want to publish this… Any review of the material should have shown the author was out of his depth—his speciality at Texas Woman’s University is Government—in this particular endeavour of proving that “mathematical formulae and the law of physics can take scholars further in deriving conclusions from sets of assumptions than can inferential statistics” (back-cover).

Thankfully, there was nothing about the Russian election! And then an unexpected piece about a tunneller and the connections drilling tunnels has with statistics and Monte Carlo simulation. As it happened, the editor of Significance ran into the engineer who eventually wrote this paper in a pub in Miami Beach during JSM 11! This reminded me of a chance encounter I had with another tunnel driller in a plane to the US, who was sitting next to me and showed me a movie of his tunneller drilling under one of the major US airports. (This must have been in 2002 as I seem to remember travelling to Banff for an IMS meeting, along with Arnaud Guillin…)

In addition, I also enjoyed the simulation challenge of reproducing every bit of each of Shakespeare’s work by [virtual] monkeys typing at random. And a bit less the simulation of Chopin’s mazurkas as the notes were written in the letter code (instead of do, ré, mi, &tc.).

The second part of Nicolas’ talk was about SMC² and hence unrelated to ABC, except that he mentioned that SMC is an (unbiased) approximation for the Metropolis-Hastings acceptance probability. Which is also an interpretation of the ideal  ABC (zero tolerance) and the noisy ABC. (Plus, Marc Beaumont is a common denominator for both perspectives!) Unfortunately, Nicolas ran out of time (because of a tight schedule) and did not give much detail on SMC². Overall, his motivational introduction was quite worth it, though! Here are his slides:

This talk also led me to reflect on the organisation of my incoming PhD class on ABC: it should include

• justify MCMC-ABC from first principles, for regular and noisy ABC;
• aggregate the literature on non-parametric justifications of ABC. The 2002 paper by Beaumont et al. remains a reference there;
• understand the link with indirect inference (the book by Gouriéroux and Monfort is sitting on my desk!);
• answer the questions “is aBc the sole and unique solution?” and “how can we evaluate/estimate the error?”;