Archive for principles of uncertainty

Practicals of Uncertainty [book review]

Posted in Books, Statistics, University life with tags , , , , , , , on December 22, 2017 by xi'an

On my way to the O’Bayes 2017 conference in Austin, I [paradoxically!] went through Jay Kadane’s Pragmatics of Uncertainty, which had been published earlier this year by CRC Press. The book is to be seen as a practical illustration of the Principles of Uncertainty Jay wrote in 2011 (and I reviewed for CHANCE). The avowed purpose is to allow the reader to check through Jay’s applied work whether or not he had “made good” on setting out clearly the motivations for his subjective Bayesian modelling. (While I presume the use of the same P of U in both books is mostly a coincidence, I started wondering how a third P of U volume could be called. Perils of Uncertainty? Peddlers of Uncertainty? The game is afoot!)

The structure of the book is a collection of fifteen case studies undertaken by Jay over the past 30 years, covering paleontology, survey sampling, legal expertises, physics, climate, and even medieval Norwegian history. Each chapter starts with a short introduction that often explains how he came by the problem (most often as an interesting PhD student consulting project at CMU), what were the difficulties in the analysis, and what became of his co-authors. As noted by the author, the main bulk of each chapter is the reprint (in a unified style) of the paper and most of these papers are actually and freely available on-line. The chapter always concludes with an epilogue (or post-mortem) that re-considers (very briefly) what had been done and what could have been done and whether or not the Bayesian perspective was useful for the problem (unsurprisingly so for the majority of the chapters!). There are also reading suggestions in the other P of U and a few exercises.

“The purpose of the book is philosophical, to address, with specific examples, the question of whether Bayesian statistics is ready for prime time. Can it be used in a variety of applied settings to address real applied problems?”

The book thus comes as a logical complement of the Principles, to demonstrate how Jay himself did apply his Bayesian principles to specific cases and how one can set the construction of a prior, of a loss function or of a statistical model in identifiable parts that can then be criticised or reanalysed. I find browsing through this series of fourteen different problems fascinating and exhilarating, while I admire the dedication of Jay to every case he presents in the book. I also feel that this comes as a perfect complement to the earlier P of U, in that it makes refering to a complete application of a given principle most straightforward, the problem being entirely described, analysed, and in most cases solved within a given chapter. A few chapters have discussions, being published in the Valencia meeting proceedings or another journal with discussions.

While all papers have been reset in the book style, I wish the graphs had been edited as well as they do not always look pretty. Although this would have implied a massive effort, it would have also been great had each chapter and problem been re-analysed or at least discussed by another fellow (?!) Bayesian in order to illustrate the impact of individual modelling sensibilities. This may however be a future project for a graduate class. Assuming all datasets are available, which is unclear from the text.

“We think however that Bayes factors are overemphasized. In the very special case in which there are only two possible “states of the world”, Bayes factors are sufficient. However in the typical case in which there are many possible states of the world, Bayes factors are sufficient only when the decision-maker’s loss has only two values.” (p. 278)

The above is in Jay’s reply to a comment from John Skilling regretting the absence of marginal likelihoods in the chapter. Reply to which I completely subscribe.

[Usual warning: this review should find its way into CHANCE book reviews at some point, with a fairly similar content.]

Bayesian program synthesis

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , on April 7, 2017 by xi'an

Last week, I—along with Jean-Michel Marin—got an email from a journalist working for Science & Vie, a French sciences journal that published a few years ago a special issue on Bayes’ theorem. (With the insane title of “the formula that deciphers the World!”) The reason for this call was the preparation of a paper on Gamalon, a new AI company that relies on (Bayesian) probabilistic programming to devise predictive tools. And spent an hour skyping with him about Bayesian inference, probabilistic programming and machine-learning, at the general level since we had not heard previously of this company or of its central tool.

“the Gamalon BPS system learns from only a few examples, not millions. It can learn using a tablet processor, not hundreds of servers. It learns right away while we play with it, not over weeks or months. And it learns from just one person, not from thousands.”

Gamalon claims to do much better than deep learning at those tasks. Not that I have reasons to doubt that claim, quite the opposite, an obvious reason being that incorporating rules and probabilistic models in the predictor is going to help if these rule and models are even moderately realistic, another major one being that handling uncertainty and learning by Bayesian tools is usually a good idea (!), and yet another significant one being that David Blei is a member of their advisory committee. But it is hard to get a feeling for such claims when the only element in the open is the use of probabilistic programming, which is an advanced and efficient manner of conducting model building and updating and handling (posterior) distributions as objects, but which does not enjoy higher predictives abilities by default. Unless I live with a restricted definition of what probabilistic programming stands for! In any case, the video provided by Gamalon and the presentation given by its CEO do not help in my understanding of the principles behind this massive gain in efficiency. Which makes sense given that the company would not want to give up their edge on the competition.

Incidentally, the video in this presentation comparing the predictive abilities of the four major astronomical explanations of the solar system is great. If not particularly connected with the difference between deep learning and Bayesian probabilistic programming.

Amazon associates links

Posted in Books, pictures with tags , , , , on December 3, 2011 by xi'an

Following a now established tradition, I give here my yearly warning that the links to Amazon.com and Amazon.fr on this blog are actually susceptible to earn me a monetary gain [of 4% to 7%] if a purchase is made in the 24 hours following the entry on Amazon through this link, thanks to the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to amazon.com/fr. As with last year, some of the items purchased through the links and contributing to my bookoholic addiction (and indirectly to the above picture) are rather unrelated with the purpose of the ‘Og, but then, anything can happen within 24 hours! Apart from a purchase I cannot decently mention here (!), here are the weirdest ones:

plus of course many more purchases of books I actually reviewed along the past months… Like six copies of Principles of uncertainty. And a dozen of the theory that would not die.