simulation as optimization [by kernel gradient descent]

Yesterday, which proved an unseasonal bright, warm, day, I biked (with a new wheel!) to the east of Paris—in the Gare de Lyon district where I lived for three years in the 1980’s—to attend a Mokaplan seminar at INRIA Paris, where Anna Korba (CREST, to which I am also affiliated) talked about sampling through optimization of discrepancies.
This proved a most formative hour as I had not seen this perspective earlier (or possibly had forgotten about it). Except through some of the talks at the Flatiron Institute on Transport, Diffusions, and Sampling last year. Incl. Marilou Gabrié’s and Arnaud Doucet’s.
The concept behind remains attractive to me, at least conceptually, since it consists in approximating the target distribution, known up to a constant (a setting I have always felt standard simulation techniques was not exploiting to the maximum) or through a sample (a setting less convincing since the sample from the target is already there), via a sequence of (particle approximated) distributions when using the discrepancy between the current distribution and the target or gradient thereof to move the particles. (With no randomness in the Kernel Stein Discrepancy Descent algorithm.)
Ana Korba spoke about practically running the algorithm, as well as about convexity properties and some convergence results (with mixed performances for the Stein kernel, as opposed to SVGD). I remain definitely curious about the method like the (ergodic) distribution of the endpoints, the actual gain against an MCMC sample when accounting for computing time, the improvement above the empirical distribution when using a sample from π and its ecdf as the substitute for π, and the meaning of an error estimation in this context.

“exponential convergence (of the KL) for the SVGD gradient flow does not hold whenever π has exponential tails and the derivatives of ∇ log π and k grow at most at a polynomial rate”

a journal of the conquest, war, famine, death[s], and chaos year

Read John Scalzi’s Head On, which is set in the same future America as Lock In, involving again the Halden syndrome patients forced to live by remotely operating robots (threeps) and introducing an extreme form of American football adapted to these patients, since they cannot be injured when their threep is. (Not as in the terrible 1975 dystopic Rollerball, which was supposedly taking place in… 2018!). The two main FBI detectives are the same as in Lock In, with great and funny dialogues but with mostly dialogues!, and a surprising disregard for team work and reporting to their hierarchy. My conclusion of the review of Lock In thus stands:

“the Halden detective conveniently happens to be the son of a very influential ex-basketball-player and hence to meet all the characters involved in the plot. This is pleasant but somewhat thin with a limited number of players considering the issues at stake and a rather artificial ending.”

Starting to cook a matcha rice pudding as an experiment, which proved successful in keeping both the matcha taste ad the rice pudding texture, and in lowering considerably the input of sugar [from which I must shy] in the recipe. (In all honesty, I actually used an organic substitute to matcha, grown and made in China!)

Found out while going to a repair shop for a brake replacement that my second bike (the one that I can leave locked in the street for a few hours!) was in such a bad state that I should not drive it. The wheels had indeed lost most of their material at the level of the brakes, due to alien, abrasive, material getting stuck inside the brake pads. My nearby repair shop was clearly uninterested in repairing a cheap, ten year old, bike and gave me a quote that was larger than my original purchase amount. I thus found a Décathlon store nearby PariSanté campus and brought back a new wheel attached to my backpack, which proved more manageable than dreaded!

Watched in the nearby cinema A Man (ある男) by Kei Ishikawa, based on a book with the same title by Keiichiro Hirano, that won the Yomiuri Prize for Literature.  (The main actress Sakura Ando also played a central role in the fantastic Cannes Palme d’Or winner Shoplifters.) I went thinking it would be a psychological thriller, but it proved me wrong, as the movie is much more about self identity, intimacy, and societal prejudices, than a detective story about usurped identity. The pace is deliberately slow and the director light, impressionist, touch gives depth and freedom to the characters, while keeping some of the mysteries behind the story open. I really enjoyed the film, which was the first time I had returned to a cinema since watching a Jim Harrison documentary in 2022. I also discovered thanks to the beginning and final scenes an infinitely deep René Magritte’s painting, La Reproduction Interdite, which I had never seen, and which was a perfect still for the film message.

mostly MC [April]

lecturing in Collège

A few weeks ago I gave a seminar on ABC (and its asymptotics) in the beautiful amphitheatre Marguerite de Navarre (a writer, a thinker, and a protector of writers like Rabelais and of reformist catholics, as well as th sister of the Collège founder, François I) of Collège de France, as complement of the lecture of that week by Stéphane Mallat, who is teaching this year on Learning and random sampling. In this lecture, Stéphane introduced Metropolis-Hastings as one can guess from the blackboards above! The amphitheatre was quite full since master students from several Parisian universities are following the course, along the “general public” since the first principle of the courses delivered at Collège de France is that they are open to everyone, free of charge and without preliminary registration! (As a countermeasure to the monopoly of the university of Paris, following the earlier example of the 1518 trilingual college of Louvain). Here are the slides I partly covered in the lecture.

the flawed genius of William Playfair [book review]

David Bellhouse has written a new book on the history of statistics, focussing on William Playfair this time (following his fantastic book on Abraham de Moivre). The Flawed Genius of William Playfair (The Story of the Father of Statistical Graphics) got published a few months ago by the University of Toronto Press.

“[Playfair] was an ideas man whose ideas often did not come to fruition; or, when they did, they withered or exploded.” [p.121]

The impressions I retained from reading this detailed account of a perfect unknown (for me) are of a rather unpleasant, unappealing, unsuccessful, fame-seeking, inefficient, short-sighted, self-aggrandising,  bigoted, dishonest, man, running from debtors for most of his life, with jail episodes for bankruptcy, while trying to make a living from all sorts of doomed enterprises, short-lived blackmailing attempts, and mediocre books that did not sell to many. Similar to David Bellhouse’s colleague earlier wondering at the appeal of exposing such a rogue character, I am left with this lingering interrogation after finishing the book…

“[Richard] Price liked what Playfair had written. He found [in 1786] Playfair to be “agreeable” and “useful”.” [p.64]

Not that I did not enjoy reading it!, as it gives a most interesting of the era between the 18th and the 19th Centuries, in particular in its detailed narration of the first months of the French Revolution of 1789, and of the impact of the Industrial Revolution on economics and politics as the birth of capitalism. The book abounds in crossing lots of historical characters, like Richard Price (Bayes’s friend who published his most famous paper), Adam Smith (whose book Playfair reprinted with poor additions), Edward Gibbons (whose book along with Smith’s inspired the title of his Inquiry Into the Permanent Causes of the Decline and Fall of Powerful and Wealthy Nations), Thomas Malthus (competing for an annotated edition of Smith’s book), not to mention the political class of Britain at the time. David Bellhouse’s book demonstrates academic and historical excellence, constantly being very detailed, with a wealth of references, documents, and definite support for or against the rumours that accompany the life and deeds of Playfair. (Frankly, rarely a name has been that inappropriate!) This includes for instance the pictures pointing out to his first (?) forged signature [p.140] and the evacuation of the myth of Playfair as a spy for the British Crown—which the Wikipedia page happily reproduces, pointing out the need for an in-depth revision of said page. Similarly, the book delivered a convincing discussion of arguments for and mostly against Playfair “being the key player in the British operation to forge [French] assignats” towards destroying its economy. A lot of the book is touching upon the then novel issue of paper money, which Playfair only and negatively considered through his own (and catastrophic) experiences. At times, the book is almost too scholarly as it makes reading less fluid than was the case his Abraham de Moivre for instance. (And obviously less than in the contemporary Jonathan Strange & Mr. Norrel!)

It may be that my very relative lack of enthusiasm stems from the realisation that the story of Playfair is overall rather little connected with statistical inference, if not with descriptive statistics (albeit with a complete disregard for the quality and sources of his data), as when  publishing a Statistical Breviary on descriptive statistics for a series of countries (and surprisingly sold on Amazon!).  Or Statistical Account of the United States of America. And of course for his innovative graphical representations like the one represented on the cover of the book or the pie chart. I feel that the book is much more engaged in Playfair’s contributions to the then nascent science of economics, as for instant about the shallow and mostly misguided views of his’ on banking and running the economy, while conducting his personal finance and investments so disastrously that it negatively advertised against confidence in such views.

On a very personal level, I noticed that some graphs were provided by my friend and statistics historian Stephen Stigler [who also wrote a review of the book] while an analysis of the poor French involved in a coding scam of Playfair about Napoléon’s escape from Elba was by Christian Genest (whom I first met at a statistics conference dinner on the Lac de Neufchâtel in 1986).

[Disclaimer about potential self-plagiarism: this post or an edited version will eventually appear in my Books Review section in CHANCE. As appropriate for a book about Chance!]