probabilistic numerics [book review]

Probabilistic numerics: Computation as machine learning is a 2022 book by Philipp Henning, Michael Osborne, and Hans Kersting that was sent to me by CUP (upon my request and almost free of charge, as I had to pay custom charges, thanks to Brexit!). With the important message of bringing statistical tools to numerics. I remember Persi Diaconis calling for (such) actions in the 1980’s (and even reading a paper of his on the topic along with George Casella in Ithaca while waiting for his car to get serviced!).

From a purely aesthetic view point, the book reads well, offers a beautiful cover and sells for a quite reasonable price for an academic book. Plus it is associated with a website containing draft version of the book. Code, links to courses, research, conferences are also available there. Just a side remark that it enjoys very wide margins that may have encouraged an inflation of footnotes (but also exercises). Except when formulas get in the way (as e.g. on p.40).

The figure below is an excerpt from the introduction that sets the scene of probabilistic numerics involving algorithms as agents, gathering data and making decisions, with an obvious analogy with standard Bayesian decision theory. Modelling uncertainty missing from the picture (if not from the book, as explained later by the authors as an argument against attaching the label Bayesian to the field). Also referring to Henri Poincaré for the origination of the prior vs posterior uncertainty about a mathematical quantity. Followed by early works from the Russian school of probability, somewhat ignored until the machine-learning revolution and a 2012 NIPS workshop organised by the authors. (I participated to a follow-up workshop at NIPS 2015.)

In this nicely written section, I have an objection to the authors’ argument that a frequentist, as opposed to a Bayesian, “has the loss function in mind from the outset” (p.9), since the loss function is logically inseparable from the prior and considered from the onset. I also like very much the conclusion to that introduction, namely that the main messages (from the book) are that (verbatim)

• classical methods are probabilist (p.10)
• numerical methods are autonomous agents (p.11)
• numerics should not be random (if not a rejection of the concept of Monte Carlo methods, p.1, but probabilistic numerics being opposed to stochastic numerics, p.67)
• numerics must report calibrated uncertainty (p.12)
• imprecise computation is to be embraced (p.12)
• probabilistic numerics consolidates numerical computation and statistical inference (p.13)
• probabilistic numerical algorithms are already adding value (p.13)
• pipelines of computation demand harmonisation

“Is it still reasonable to labour under computational constraints conceived in the 1940s?” (p.113)

“rather than being equally good for any number of dimensions, Monte Carlo is perhaps better thought of as being equally bad” (p.110)

Chapter I is a 40p infodump (!) on mathematical concepts needed for the following parts. Chapter II is about integration, opposing again PN and Monte Carlo (with strange remark that MCMC does not achieve √N convergence rate, p.72). In the sense that the later is frequentist in that it does not use a prior [unless considering a limiting improper version as in Section 12.2, an intriguing concept in this setup as I wonder whether or not improper priors can at all be contemplated] on the object of interest and hence that the stochasticity does not reflect uncertainty but rather the impact of the simulated sample. Advocating Bayesian quadrature (with some weird convergence graphs exhibiting a high variability with the number of iterations that apparently is not discussed) and bringing in the fascinating perspective of model choice in that framework (leading to compute a posterior probability for each model!). Being evidently biased towards Monte Carlo, I find the opposition in Chapter 12 unnecessarily antagonistic, while presenting Monte Carlo methods as a form of minimax solution, the more because quasi-Monte Carlo methods are hardly discussed (or dismissed). As illustrated by the following picture (p.115) and the above quotes. (And I won’t even go into the absurdity of §12.3 trashing pseudo-random generators as “painfully dumb”.)

Chapter III is a sort of dual of Chapter II for linear algebra numerics, primarily solving linear equations by Gaussian solvers, which introduces new concepts like Krylov sequences, although it sounds quite specific (for an outsider like me). Chapters IV and V deal with the more ambitious prospect of optimisation. Reconsidering classics and expanding into Bayesian optimisation, using Gaussian process priors and defining specific loss functions. Bringing in a strong link with machine learning tools and goals. [citation typo on p.277]. Chapter VII addresses the resolution of ODEs by a Bayesian state space model representation and (again!) Gaussian processes. Reaching to mentioning inverse problems and offering a short finale on prospective steps for interested readers.

[Disclaimer about potential self-plagiarism: this post or an edited version will eventually appear in my Books Review section in CHANCE.]

linearity, reversed

While answering a question on X validated on the posterior mean being a weighted sum of the prior mean and of the maximum likelihood estimator, when the weights do not depend on the data, which is true in conjugate natural exponential family settings, I re-read this wonderful 1979 paper of Diaconis & Ylvisaker establishing the converse, namely that when the linear combination holds, the prior need be conjugate! This holds within exponential families, but I cannot think of a reasonable case outside exponential families where the linearity holds (again with constant weights, as otherwise it always holds in dimension one, albeit with weights possibly outside [0,1]).

prime suspects [book review]

I was contacted by Princeton University Press to comment on the comic book/graphic novel Prime Suspects (The Anatomy of Integers and Permutations), by Andrew Granville (mathematician) & Jennifer Granville (writer), and Robert Lewis (illustrator), and they sent me the book. I am not a big fan of graphic book entries to mathematical even less than to statistical notions (Logicomix being sort of an exception for its historical perspective and nice drawing style) and this book did nothing to change my perspective on the subject. First, the plot is mostly a pretense at introducing number theory concepts and I found it hard to follow it for more than a few pages. The [noires maths] story is that “forensic maths” detectives are looking at murders that connects prime integers and permutations… The ensuing NCIS-style investigation gives the authors the opportunity to skim through the whole cenacle of number theorists, plus a few other mathematicians, who appear as more or less central characters. Even illusory ones like Nicolas Bourbaki. And Alexander Grothendieck as a recluse and clairvoyant hermit [who in real life did not live in a Pyrénées cavern!!!]. Second, I [and nor is Andrew who was in my office when the book arrived!] am not particularly enjoying the drawings or the page composition or the colours of this graphic novel, especially because I find the characters drawn quite inconsistently from one strip to the next, to the point of being unrecognisable, and, if it matters, hardly resembling their real-world equivalent (as seen in the portrait of Persi Diaconis). To be completely honest, the drawings look both ugly and very conventional to me, in that I do not find much of a characteristic style to them. To contemplate what Jacques Tardi,  François Schuiten or José Muñoz could have achieved with the same material… (Or even Edmond Baudoin, who drew the strips for the graphic novels he coauthored with Cédric Villani.) The graphic novel (with a prime 181 pages) is postfaced with explanations about the true persons behind the characters, from Carl Friedriech Gauß to Terry Tao, and of course on the mathematical theory for the analogies between the prime and cycles frequencies behind the story. Which I find much more interesting and readable, obviously. (With a surprise appearance of Kingman’s coalescent!) But also somewhat self-defeating in that so much has to be explained on the side for the links between the story, the characters and the background heavily loaded with “obscure references” to make sense to more than a few mathematician readers. Who may prove to be the core readership of this book.

There is also a bit of a Gödel-Escher-and-Bach flavour in that a piece by Robert Schneider called Réverie in Prime Time Signature is included, while an Escher’s infinite stairway appears in one page, not far from what looks like Milano Vittorio Emmanuelle gallery (On the side, I am puzzled by the footnote on p.208 that “I should clarify that selecting a random permutation and a random prime, as described, can be done easily, quickly, and correctly”. This may be connected to the fact that the description of Bach’s algorithm provided therein is incomplete.)

[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!]

Bayesian goodness of fit

 

Persi Diaconis and Guanyang Wang have just arXived an interesting reflection on the notion of Bayesian goodness of fit tests. Which is a notion that has always bothered me, in a rather positive sense (!), as

“I also have to confess at the outset to the zeal of a convert, a born again believer in stochastic methods. Last week, Dave Wright reminded me of the advice I had given a graduate student during my algebraic geometry days in the 70’s :`Good Grief, don’t waste your time studying statistics. It’s all cookbook nonsense.’ I take it back! …” David Mumford

The paper starts with a reference to David Mumford, whose paper with Wu and Zhou on exponential “maximum entropy” synthetic distributions is at the source (?) of this paper, and whose name appears in its very title: “A conversation for David Mumford”…, about his conversion from pure (algebraic) maths to applied maths. The issue of (Bayesian) goodness of fit is addressed, with card shuffling examples, the null hypothesis being that the permutation resulting from the shuffling is uniformly distributed if shuffling takes enough time. Interestingly, while the parameter space is compact as a distribution on a finite set, Lindley’s paradox still occurs, namely that the null (the permutation comes from a Uniform) is always accepted provided there is no repetition under a “flat prior”, which is the Dirichlet D(1,…,1) over all permutations. (In this finite setting an improper prior is definitely improper as it does not get proper after accounting for observations. Although I do not understand why the Jeffreys prior is not the Dirichlet(½,…,½) in this case…) When resorting to the exponential family of distributions entertained by Zhou, Wu and Mumford, including the uniform distribution as one of its members, Diaconis and Wang advocate the use of a conjugate prior (exponential family, right?!) to compute a Bayes factor that simplifies into a ratio of two intractable normalising constants. For which the authors suggest using importance sampling, thermodynamic integration, or the exchange algorithm. Except that they rely on the (dreaded) harmonic mean estimator for computing the Bayes factor in the following illustrative section! Due to the finite nature of the space, I presume this estimator still has a finite variance. (Remark 1 calls for convergence results on exchange algorithms, which can be found I think in the just as recent arXival by Christophe Andrieu and co-authors.) An interesting if rare feature of the example processed in the paper is that the sufficient statistic used for the permutation model can be directly simulated from a Multinomial distribution. This is rare as seen when considering the benchmark of Ising models, for which the summary and sufficient statistic cannot be directly simulated. (If only…!) In fine, while I enjoyed the paper a lot, I remain uncertain as to its bearings, since defining an objective alternative for the goodness-of-fit test becomes quickly challenging outside simple enough models.

10 great ideas about chance [book preview]

[As I happened to be a reviewer of this book by Persi Diaconis and Brian Skyrms, I had the opportunity (and privilege!) to go through its earlier version. Here are the [edited] comments I sent back to PUP and the authors about this earlier version. All in  all, a terrific book!!!]

The historical introduction (“measurement”) of this book is most interesting, especially its analogy of chance with length. I would have appreciated a connection earlier than Cardano, like some of the Greek philosophers even though I gladly discovered there that Cardano was not only responsible for the closed form solutions to the third degree equation. I would also have liked to see more comments on the vexing issue of equiprobability: we all spend (if not waste) hours in the classroom explaining to (or arguing with) students why their solution is not correct. And they sometimes never get it! [And we sometimes get it wrong as well..!] Why is such a simple concept so hard to explicit? In short, but this is nothing but a personal choice, I would have made the chapter more conceptual and less chronologically historical.

“Coherence is again a question of consistent evaluations of a betting arrangement that can be implemented in alternative ways.” (p.46)

The second chapter, about Frank Ramsey, is interesting, if only because it puts this “man of genius” back under the spotlight when he has all but been forgotten. (At least in my circles.) And for joining probability and utility together. And for postulating that probability can be derived from expectations rather than the opposite. Even though betting or gambling has a (negative) stigma in many cultures. At least gambling for money, since most of our actions involve some degree of betting. But not in a rational or reasoned manner. (Of course, this is not a mathematical but rather a psychological objection.) Further, the justification through betting is somewhat tautological in that it assumes probabilities are true probabilities from the start. For instance, the Dutch book example on p.39 produces a gain of .2 only if the probabilities are correct.

> gain=rep(0,1e4)
> for (t in 1:1e4){
+ p=rexp(3);p=p/sum(p)
+ gain[t]=(p[1]*(1-.6)+p[2]*(1-.2)+p[3]*(.9-1))/sum(p)}
> hist(gain)

As I made it clear at the BFF4 conference last Spring, I now realise I have never really adhered to the Dutch book argument. This may be why I find the chapter somewhat unbalanced with not enough written on utilities and too much on Dutch books.

“The force of accumulating evidence made it less and less plausible to hold that subjective probability is, in general, approximate psychology.” (p.55)

A chapter on “psychology” may come as a surprise, but I feel a posteriori that it is appropriate. Most of it is about the Allais paradox. Plus entries on Ellesberg’s distinction between risk and uncertainty, with only the former being quantifiable by “objective” probabilities. And on Tversky’s and Kahneman’s distinction between heuristics, and the framing effect, i.e., how the way propositions are expressed impacts the choice of decision makers. However, it is leaving me unclear about the conclusion that the fact that people behave irrationally should not prevent a reliance on utility theory. Unclear because when taking actions involving other actors their potentially irrational choices should also be taken into account. (This is mostly nitpicking.)

“This is Bernoulli’s swindle. Try to make it precise and it falls apart. The conditional probabilities go in different directions, the desired intervals are of different quantities, and the desired probabilities are different probabilities.” (p.66)

The next chapter (“frequency”) is about Bernoulli’s Law of Large numbers and the stabilisation of frequencies, with von Mises making it the basis of his approach to probability. And Birkhoff’s extension which is capital for the development of stochastic processes. And later for MCMC. I like the notions of “disreputable twin” (p.63) and “Bernoulli’s swindle” about the idea that “chance is frequency”. The authors call the identification of probabilities as limits of frequencies Bernoulli‘s swindle, because it cannot handle zero probability events. With a nice link with the testing fallacy of equating rejection of the null with acceptance of the alternative. And an interesting description as to how Venn perceived the fallacy but could not overcome it: “If Venn’s theory appears to be full of holes, it is to his credit that he saw them himself.” The description of von Mises’ Kollectiven [and the welcome intervention of Abraham Wald] clarifies my previous and partial understanding of the notion, although I am unsure it is that clear for all potential readers. I also appreciate the connection with the very notion of randomness which has not yet found I fear a satisfactory definition. This chapter asks more (interesting) questions than it brings answers (to those or others). But enough, this is a brilliant chapter!

“…a random variable, the notion that Kac found mysterious in early expositions of probability theory.” (p.87)

Chapter 5 (“mathematics”) is very important [from my perspective] in that it justifies the necessity to associate measure theory with probability if one wishes to evolve further than urns and dices. To entitle Kolmogorov to posit his axioms of probability. And to define properly conditional probabilities as random variables (as my third students fail to realise). I enjoyed very much reading this chapter, but it may prove difficult to read for readers with no or little background in measure (although some advanced mathematical details have vanished from the published version). Still, this chapter constitutes a strong argument for preserving measure theory courses in graduate programs. As an aside, I find it amazing that mathematicians (even Kac!) had not at first realised the connection between measure theory and probability (p.84), but maybe not so amazing given the difficulty many still have with the notion of conditional probability. (Now, I would have liked to see some description of Borel’s paradox when it is mentioned (p.89).

“Nothing hangs on a flat prior (…) Nothing hangs on a unique quantification of ignorance.” (p.115)

The following chapter (“inverse inference”) is about Thomas Bayes and his posthumous theorem, with an introduction setting the theorem at the centre of the Hume-Price-Bayes triangle. (It is nice that the authors include a picture of the original version of the essay, as the initial title is much more explicit than the published version!) A short coverage, in tune with the fact that Bayes only contributed a twenty-plus paper to the field. And to be logically followed by a second part [formerly another chapter] on Pierre-Simon Laplace, both parts focussing on the selection of prior distributions on the probability of a Binomial (coin tossing) distribution. Emerging into a discussion of the position of statistics within or even outside mathematics. (And the assertion that Fisher was the Einstein of Statistics on p.120 may be disputed by many readers!)

“So it is perfectly legitimate to use Bayes’ mathematics even if we believe that chance does not exist.” (p.124)

The seventh chapter is about Bruno de Finetti with his astounding representation of exchangeable sequences as being mixtures of iid sequences. Defining an implicit prior on the side. While the description sticks to binary events, it gets quickly more advanced with the notion of partial and Markov exchangeability. With the most interesting connection between those exchangeabilities and sufficiency. (I would however disagree with the statement that “Bayes was the father of parametric Bayesian analysis” [p.133] as this is extrapolating too much from the Essay.) My next remark may be non-sensical, but I would have welcomed an entry at the end of the chapter on cases where the exchangeability representation fails, for instance those cases when there is no sufficiency structure to exploit in the model. A bonus to the chapter is a description of Birkhoff’s ergodic theorem “as a generalisation of de Finetti” (p..134-136), plus half a dozen pages of appendices on more technical aspects of de Finetti’s theorem.

“We want random sequences to pass all tests of randomness, with tests being computationally implemented”. (p.151)

The eighth chapter (“algorithmic randomness”) comes (again!) as a surprise as it centres on the character of Per Martin-Löf who is little known in statistics circles. (The chapter starts with a picture of him with the iconic Oberwolfach sculpture in the background.) Martin-Löf’s work concentrates on the notion of randomness, in a mathematical rather than probabilistic sense, and on the algorithmic consequences. I like very much the section on random generators. Including a mention of our old friend RANDU, the 16 planes random generator! This chapter connects with Chapter 4 since von Mises also attempted to define a random sequence. To the point it feels slightly repetitive (for instance Jean Ville is mentioned in rather similar terms in both chapters). Martin-Löf’s central notion is computability, which forces us to visit Turing’s machine. And its role in the undecidability of some logical statements. And Church’s recursive functions. (With a link not exploited here to the notion of probabilistic programming, where one language is actually named Church, after Alonzo Church.) Back to Martin-Löf, (I do not see how his test for randomness can be implemented on a real machine as the whole test requires going through the entire sequence: since this notion connects with von Mises’ Kollektivs, I am missing the point!) And then Kolmororov is brought back with his own notion of complexity (which is also Chaitin’s and Solomonov’s). Overall this is a pretty hard chapter both because of the notions it introduces and because I do not feel it is completely conclusive about the notion(s) of randomness. A side remark about casino hustlers and their “exploitation” of weak random generators: I believe Jeff Rosenthal has a similar if maybe simpler story in his book about Canadian lotteries.

“Does quantum mechanics need a different notion of probability? We think not.” (p.180)

The penultimate chapter is about Boltzmann and the notion of “physical chance”. Or statistical physics. A story that involves Zermelo and Poincaré, And Gibbs, Maxwell and the Ehrenfests. The discussion focus on the definition of probability in a thermodynamic setting, opposing time frequencies to space frequencies. Which requires ergodicity and hence Birkhoff [no surprise, this is about ergodicity!] as well as von Neumann. This reaches a point where conjectures in the theory are yet open. What I always (if presumably naïvely) find fascinating in this topic is the fact that ergodicity operates without requiring randomness. Dynamical systems can enjoy ergodic theorem, while being completely deterministic.) This chapter also discusses quantum mechanics, which main tenet requires probability. Which needs to be defined, from a frequency or a subjective perspective. And the Bernoulli shift that brings us back to random generators. The authors briefly mention the Einstein-Podolsky-Rosen paradox, which sounds more metaphysical than mathematical in my opinion, although they get to great details to explain Bell’s conclusion that quantum theory leads to a mathematical impossibility (but they lost me along the way). Except that we “are left with quantum probabilities” (p.183). And the chapter leaves me still uncertain as to why statistical mechanics carries the label statistical. As it does not seem to involve inference at all.

“If you don’t like calling these ignorance priors on the ground that they may be sharply peaked, call them nondogmatic priors or skeptical priors, because these priors are quite in the spirit of ancient skepticism.” (p.199)

And then the last chapter (“induction”) brings us back to Hume and the 18th Century, where somehow “everything” [including statistics] started! Except that Hume’s strong scepticism (or skepticism) makes induction seemingly impossible. (A perspective with which I agree to some extent, if not to Keynes’ extreme version, when considering for instance financial time series as stationary. And a reason why I do not see the criticisms contained in the Black Swan as pertinent because they savage normality while accepting stationarity.) The chapter rediscusses Bayes’ and Laplace’s contributions to inference as well, challenging Hume’s conclusion of the impossibility to finer. Even though the representation of ignorance is not unique (p.199). And the authors call again for de Finetti’s representation theorem as bypassing the issue of whether or not there is such a thing as chance. And escaping inductive scepticism. (The section about Goodman’s grue hypothesis is somewhat distracting, maybe because I have always found it quite artificial and based on a linguistic pun rather than a logical contradiction.) The part about (Richard) Jeffrey is quite new to me but ends up quite abruptly! Similarly about Popper and his exclusion of induction. From this chapter, I appreciated very much the section on skeptical priors and its analysis from a meta-probabilist perspective.

There is no conclusion to the book, but to end up with a chapter on induction seems quite appropriate. (But there is an appendix as a probability tutorial, mentioning Monte Carlo resolutions. Plus notes on all chapters. And a commented bibliography.) Definitely recommended!

[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!]