I’m getting the point

A long-winded X validated discussion on the [textbook] mean-variance conjugate posterior for the Normal model left me [mildly] depressed at the point and use of answering questions on this forum. Especially as it came at the same time as a catastrophic outcome for my mathematical statistics exam.  Possibly an incentive to quit X validated as one quits smoking, although this is not the first attempt…

AIQ [book review]

AIQ was my Christmas day read, which I mostly read while the rest of the household was still sleeping. The book, written by two Bayesians, Nick Polson and James Scott, was published before the ISBA meeting last year, but I only bought it on my last trip to Warwick [as a Xmas present]. This is a pleasant book to read, especially while drinking tea by the fire!, well-written and full of facts and anecdotes I did not know or had forgotten (more below). Intended for a general audience, it is also quite light, from a technical side, rather obviously, but also from a philosophical side. While strongly positivist about the potential of AIs for the general good, it cannot be seen as an antidote to the doomlike Superintelligence by Nick Bostrom or the more factual Weapons of Maths Destruction by Cathy O’Neal. (Both commented on the ‘Og.)

Indeed, I find the book quite benevolent and maybe a wee bit too rosy in its assessment of AIs and the discussion on how Facebook and Russian intervention may have significantly to turn the White House Orange is missing [imho] the viral nature of the game, when endless loops of highly targeted posts can cut people from the most basic common sense. While the authors are “optimistic that, given the chance, people can be smart enough”, I do reflect on the sheer fact that the hoax that Hillary Clinton was involved in a child sex ring was ever considered seriously by people. To the point of someone shooting at the pizza restaurant. And I hence am much less optimistic at the ability for a large enough portion of the population, not even the majority, to keep a critical distance from the message carried by AI driven media. Similarly, while Nick and James point out (rather late in the book) that big data (meaning large data) is not necessarily good data for being unrepresentative at the population at large, they do not propose (in the book) highly convincing solutions to battle bias in existing and incoming AIs. Leading to a global worry that AIs may do well for a majority of the population and discriminate against a minority by the same reasoning. As described in Cathy O’Neal‘s book, and elsewhere, proprietary software does not even have to explain why it discriminates. More globally, the business school environment of the authors may have prevented them from stating a worry on the massive power grab by the AI-based companies, which genetically grow with little interest in democracy and states, as shown (again) by the recent election or their systematic fiscal optimisation. Or by the massive recourse to machine learning by Chinese authorities towards a social credit system grade for all citizens.

“La rage de vouloir conclure est une des manies les plus funestes et les plus stériles qui appartiennent à l’humanité. Chaque religion et chaque philosophie a prétendu avoir Dieu à elle, toiser l’infini et connaître la recette du bonheur.” Gustave Flaubert

I did not know about Henrietta Leavitt’s prediction rule for pulsating stars, behind Hubble’s discovery, which sounds like an astronomy dual to Rosalind Franklin’s DNA contribution. The use of Bayes’ rule for locating lost vessels is also found in The Theorem that would not die. Although I would have also mentioned its failure in locating Malaysia Airlines Flight 370. I had also never heard the great expression of “model rust. Nor the above quote from Flaubert. It seems I have recently spotted the story on how a 180⁰ switch in perspective on language understanding by machines brought the massive improvement that we witness today. But I cannot remember where. And I have also read about Newton missing the boat on the precision of the coinage accuracy (was it in Bryson’s book on the Royal Society?!), but with less neutral views on the role of Newton in the matter, as the Laplace of England would have benefited from keeping the lax measures of assessment.

Great to see friendly figures like Luke Bornn and Katherine Heller appearing in the pages. Luke for his work on the statistical analysis of basketball games, Katherine  for her work on predictive analytics in medicine. Reflecting on the missed opportunities represented by the accumulation of data on any patient throughout their life that is as grossly ignored nowadays as it was at Nightingale‘s time. The message of the chapter [on “The Lady with the Lamp”] may again be somewhat over-optimistic: while AI and health companies see clear incentives in developing more encompassing prediction and diagnostic techniques, this will only benefit patients who can afford the ensuing care. Which, given the state of health care systems in the most developed countries, is an decreasing proportion. Not to mention the less developed countries.

Overall, a nice read for the general public, de-dramatising the rise of the machines!, and mixing statistics and machine learning to explain the (human) intelligence behind the AIs. Nothing on the technical side, to be sure, but this was not the intention of the authors.

Binomial vs Bernoulli

An interesting confusion on X validated where someone was convinced that using the Bernoulli representation of a sequence of Bernoulli experiments led to different posterior probabilities of two possible models than when using their Binomial representation. The confusion actually stemmed from using different conditionals, namely N¹=4,N²=1 in the first case (for a model M¹ with two probabilities p¹ and p²) and N¹+N²=5 in the second case (for a model M² with a single probability p⁰). While (N¹,N²) is sufficient for the first model and N¹+N² is sufficient for the second model, P(M¹|N¹,N²) is not commensurable to P(M²|N¹+N²)! Another illustration of the fickleness of the notion of sufficiency when comparing models.

at CIRM [jatp]

a jump back in time

As the Department of Statistics in Warwick is slowly emptying its shelves and offices for the big migration to the new building that is almost completed, books and documents are abandoned in the corridors and the work spaces. On this occasion, I thus happened to spot a vintage edition of the Valencia 3 proceedings. I had missed this meeting and hence the volume for, during the last year of my PhD, I was drafted in the French Navy and as a result prohibited to travel abroad. (Although on reflection I could have safely done it with no one in the military the wiser!) Reading through the papers thirty years later is a weird experience, as I do not remember most of the papers, the exception being the mixture modelling paper by José Bernardo and Javier Giròn which I studied a few years later when writing the mixture estimation and simulation paper with Jean Diebolt. And then again in our much more recent non-informative paper with Clara Grazian.  And Prem Goel’s survey of Bayesian software. That is, 1987 state of the art software. Covering an amazing eighteen list. Including versions by Zellner, Tierney, Schervish, Smith [but no MCMC], Jaynes, Goldstein, Geweke, van Dijk, Bauwens, which apparently did not survive the ages till now. Most were in Fortran but S was also mentioned. And another version of Tierney, Kass and Kadane on Laplace approximations. And the reference paper of Dennis Lindley [who was already retired from UCL at that time!] on the Hardy-Weinberg equilibrium. And another paper by Don Rubin on using SIR (Rubin, 1983) for simulating from posterior distributions with missing data. Ten years before the particle filter paper, and apparently missing the possibility of weights with infinite variance.

There already were some illustrations of Bayesian analysis in action, including one by Jay Kadane reproduced in his book. And several papers by Jim Berger, Tony O’Hagan, Luis Pericchi and others on imprecise Bayesian modelling, which was in tune with the era, the imprecise probability book by Peter Walley about to appear. And a paper by Shaw on numerical integration that mentioned quasi-random methods. Applied to a 12 component Normal mixture.Overall, a much less theoretical content than I would have expected. And nothing about shrinkage estimators, although a fraction of the speakers had worked on this topic most recently.

At a less fundamental level, this was a time when LaTeX was becoming a standard, as shown by a few papers in the volume (and as I was to find when visiting Purdue the year after), even though most were still typed on a typewriter, including a manuscript addition by Dennis Lindley. And Warwick appeared as a Bayesian hotpot!, with at least five papers written by people there permanently or on a long term visit. (In case a local is interested in it, I have kept the volume, to be found in my new office!)

Bayesian statistics in the big data era

In conjunction with Kerrie Mengersen’ Jean Morlet Chair at CIRM, Luminy, Marseilles, we organise a special conference “Bayesian statistics in the big data era” on November 26-30, 2018, with the following speakers having already confirmed attendance

Louis Aslett (Durham, UK)
Sudipto Banerjee (UCLA, US)
Tamara Broderick (MIT, US)
Noël Cressie (Wollongong, OZ)
Marco Cuturi (ENSAE, FR)
David Dunson (Duke, US)
Sylvia Frühwirth-Schnatter (WU, AU)
Amy Herring (Duke, US)
Gregor Kastner (WU, AU)
Ruth King (Edinburgh, UK)

Gary Koop (Edinburgh, UK)
Antonio Lijoi (Bocconi, IT)
Jean-Michel Marin (Montpellier, FR)
Antonietta Mira (Lugano, CH)
Peter Müller (UT Austin, US)
Igor Pruenster (Bocconi, IT)
Stéphane Robin (INRA, FR)
Heejung Shim (U Melbourne, OZ)
Minh-Ngoc Tran (UNSW, OZ)
Darren Wilkinson (Newcastle, UK)

(more)

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Registration is free but compulsory, and we encourage all interested data scientists (and beyond) to apply and to contribute a talk or a poster. The size of the audience is limited to a maximum of 80 participants, on a first-come first-serve basis. (Cheap housing is available on the campus, located in the gorgeous national park des Calanques south of Marseilles.)

In connection with this conference, there will be a workshop the previous weekend on “Young Bayesians and Big Data for social good”, to get junior researchers interested in the analysis of data related with social issues and human rights to work with a few senior researchers. More details soon, here and on the CIRM website.

Masterclass in Bayesian Statistics in Marseilles next Fall

This post is to announce a second occurrence of the exciting “masterclass in Bayesian Statistics” that we organised in 2016, near Marseilles. It will take place on 22-26 October 2018 once more at CIRM (Centre International de Recherches Mathématiques, Luminy, Marseilles, France). The targeted audience includes all scientists interested in learning how Bayesian inference may be used to tackle the practical problems they face in their own research. In particular PhD students and post-docs should benefit most directly from this masterclass. Among the invited speakers, Kerrie Mengersen from QUT, Brisbane, visiting Marseilles this Fall, will deliver a series of lectures on the interface between Bayesian statistics and applied modelling, Havard Rue from KAUST will talk on computing with INLA, and Aki Vehtari from Aalto U, Helsinki, will give a course on Bayesian model assessment and model choice. There will be two tutorials on R and on Stan.

All interested participants in this masterclass should pre-register as early as possible, given that the total attendance is limited to roughly 90 participants. Some specific funding for local expenses (i.e., food + accommodation on-siteat CIRM) is available (thanks to CIRM, and potentially to Fondation Jacques Hadamard, to be confirmed); this funding will be attributed by the scientific committee, with high priority to PhD students and post-docs.