“Boots charges £28.25 for Levonelle emergency contraceptive (the leading brand) and £26.75 for its own generic version. Tesco now charges £13.50 for Levonelle and Superdrug £13.49 for a generic version. In France, the tablet costs £5.50.”The Guardian, July 20, 2017

## Boots is deliberately overcharging for the morning-after pill!

Posted in Kids, pictures with tags #justsaynon, boots, boycott, morning-after pill, United Kingdom on July 21, 2017 by xi'an## and the travelling salesman is…

Posted in Books, pictures, Statistics, University life with tags Alan Turing, PCI Comput Stats, PCI Evol Biol, Peer Community on July 21, 2017 by xi'an**H**ere is another attempt at using StippleGen on… Alan Turing‘s picture. My reason for attempting a travelling salesman rendering of this well-known picture towards creating a logo for PCI Comput Stats, the peer community project I am working on this summer. With the help of the originators of PCI Evol Biol.

## vendanges tardives

Posted in Kids, pictures, Wines with tags Alsace, French wines, Gewurtzstraminer, late harvest wine, vendanges tardives on July 20, 2017 by xi'an## Midsummer dinner at Emmanuel College

Posted in Kids, pictures, Travel, University life, Wines with tags Bourgogne, Cambridge colleges, Chassagne-Montrachet, Emmanuel College, England, Fareoe Islands, French wines, Sauternes, Shropshire cheese, skuas on July 20, 2017 by xi'anIt just so happened that I was in Cambridge for the Midsummer dinner last Saturday at Emmanuel College and that a good friend, who happens to be a Fellow of that College, invited me to the dinner. Making the second dinner in a Cambridge college in a week, after the workshop dinner at Trinity. Except the one at Emmanuel was a much more formal affair, with dress requirement (!) and elaborate dishes. The wines were also exceptional, with a remarkable 2002 Chassagne-Montrachet.While the dinning room (or whatever it is called) is beautiful, it is also rather noisy and I could not engage in conversation with anyone but my immediate neighbours, but still managed to have a fairly interesting exchange with a biologist studying skuas on the Faroe Islands. The end of the meal was announced by a loud clap and Graces in Latin, followed by cheese and port (and a fabulous Sauternes!, not in the wine list) in an equally beautiful room, where it was easier to talk with my neighbours. All in all, a unique evening and opportunity for a glimpse into College traditions! [And a first wine post for the 20th of the month!!]

## what makes variables randoms [book review]

Posted in Books, Mountains, Statistics with tags Bayesian Analysis, Bertrand's paradox, conditional probability, introductory textbooks, σ-algebra, Lebesgue integration, Riemann integration on July 19, 2017 by xi'an**W**hen the goal of a book is to make measure theoretic probability available to applied researchers for conducting their research, I cannot but applaud! Peter Veazie’s goal of writing “a brief text that provides a basic conceptual introduction to measure theory” (p.4) is hence most commendable. Before reading What makes variables random, I was uncertain how this could be achieved with a limited calculus background, given the difficulties met by our third year maths students. After reading the book, I am even less certain this is feasible!

“…it is the data generating process that makes the variables random and not the data.”

Chapter 2 is about basic notions of set theory. Chapter 3 defines measurable sets and measurable functions and integrals against a given measure μ as

which I find particularly unnatural compared with the definition through simple functions (esp. because it does not tell how to handle 0x∞). The ensuing discussion shows the limitation of the exercise in that the definition is only explained for finite sets (since the notion of a partition achieving the supremum on page 29 is otherwise meaningless). A generic problem with the book, in that most examples in the probability section relate to discrete settings (see the discussion of the power set p.66). I also did not see a justification as to why measurable functions enjoy well-defined integrals in the above sense. All in all, to see less than ten pages allocated to measure theory *per se* is rather staggering! For instance,

does not appear to be defined at all.

“…the mathematical probability theory underlying our analyses is just mathematics…”

Chapter 4 moves to probability measures. It distinguishes between objective (or frequentist) and subjective measures, which is of course open to diverse interpretations. And the definition of a conditional measure is the traditional one, conditional on a set rather than on a σ-algebra. Surprisingly as this is in my opinion one major reason for using measures in probability theory. And avoids unpleasant issues such as Bertrand’s paradox. While random variables are defined in the standard sense of real valued measurable functions, I did not see a definition of a continuous random variables or of the Lebesgue measure. And there are only a few lines (p.48) about the notion of expectation, which is so central to measure-theoretic probability as to provide a way of entry into measure theory! Progressing further, the σ-algebra induced by a random variable is defined as a partition (p.52), a particularly obscure notion for continuous rv’s. When the conditional density of one random variable given the realisation of another is finally introduced (p.63), as an expectation reconciling with the set-wise definition of conditional probabilities, it is in a fairly convoluted way that I fear will scare newcomers out of their wit. Since it relies on a sequence of nested sets with positive measure, implying an underlying topology and the like, which somewhat shows the impossibility of the overall task…

“In the Bayesian analysis, the likelihood provides meaning to the posterior.”

Statistics is hurriedly introduced in a short section at the end of Chapter 4, assuming the notion of likelihood is already known by the readers. But nitpicking (p.65) at the representation of the terms in the log-likelihood as depending on an unspecified parameter value θ [not to be confused with the data-generating value of θ, which does not appear clearly in this section]. Section that manages to include arcane remarks distinguishing maximum likelihood estimation from Bayesian analysis, all this within a page! (Nowhere is the Bayesian perspective clearly defined.)

“We should no more perform an analysis clustered by state than we would cluster by age, income, or other random variable.”

The last part of the book is about probabilistic models, drawing a distinction between data generating process models and data models (p.89), by which the author means the hypothesised probabilistic model versus the empirical or bootstrap distribution. An interesting way to relate to the main thread, except that the convergence of the data distribution to the data generating process model cannot be established at this level. And hence that the very nature of bootstrap may be lost on the reader. A second and final chapter covers some common or vexing problems and the author’s approach to them. Revolving around standard errors, fixed and random effects. The distinction between standard deviation (“a mathematical property of a probability distribution”) and standard error (“representation of variation due to a data generating process”) that is followed for several pages seems to boil down to a possible (and likely) model mis-specification. The chapter also contains an extensive discussion of notations, like indexes (or indicators), which seems a strange focus esp. at this location in the book. Over 15 pages! (Furthermore, I find quite confusing that a set of indices is denoted there by the double barred I, usually employed for the indicator function.)

“…the reader will probably observe the conspicuous absence of a time-honoured topic in calculus courses, the “Riemann integral”… Only the stubborn conservatism of academic tradition could freeze it into a regular part of the curriculum, long after it had outlived its historical importance.”Jean Dieudonné,Foundations of Modern Analysis

In conclusion, I do not see the point of this book, from its insistence on measure theory that never concretises for lack of mathematical material to an absence of convincing examples as to why this is useful for the applied researcher, to the intended audience which is expected to already quite a lot about probability and statistics, to a final meandering around linear models that seems at odds with the remainder of What makes variables random, without providing an answer to this question. Or to the more relevant one of why Lebesgue integration is preferable to Riemann integration. (Not that there does not exist convincing replies to this question!)

## who’s that travelling salesman path?!

Posted in Statistics with tags image processing, puzzle, StippleGen, travelling salesman on July 18, 2017 by xi'an## ABC at sea and at war

Posted in Books, pictures, Statistics, Travel with tags ABC, Approximate Bayesian computation, Battle of the Dogger Bank, counterfactuals, crêpes, first World War, history, Jutland, naval battle, Significance, The Fog of War, wargame on July 18, 2017 by xi'an**W**hile preparing crêpes at home yesterday night, I browsed through the most recent issue of Significance and among many goodies, I spotted an article by McKay and co-authors discussing the simulation of a British vs. German naval battle from the First World War I had never heard of, the Battle of the Dogger Bank. The article was illustrated by a few historical pictures, but I quickly came across a more statistical description of the problem, which was not about creating wargames and alternate realities but rather inferring about the likelihood of the actual income, i.e., whether or not the naval battle outcome [which could be seen as a British victory, ending up with 0 to 1 sunk boat] was either a lucky strike or to be expected. And the method behind solving this question was indeed both Bayesian and ABC-esque! I did not read the longer paper by McKay et al. (hard to do while flipping crêpes!) but the description in Significance was clear enough to understand that the six summary statistics used in this ABC implementation were the number of shots, hits, and lost turrets for both sides. (The answer to the original question is that indeed the British fleet was lucky to keep all its boats afloat. But it is also unlikely another score would have changed the outcome of WWI.) [As I found in this other history paper, ABC seems quite popular in historical inference! And there is another completely unrelated arXived paper with main title The Fog of War…]