## Archive for relativity

## black holes capture Nobel

Posted in Statistics, Travel, University life with tags astronomy, Astrophysics, black holes, Garching, Max Planck Institute, Milky Way, Nobel Prize, relativity on October 7, 2020 by xi'an## meet the black heart of Messier

Posted in pictures, Travel, University life with tags Astrophysics, Atacama, black holes, ERC, Event Horizon Telescope, galaxy Messier 87, M87, NSF, relativity, The Astrophysical Journal Letters on April 10, 2019 by xi'an## the explanation why Science gets underfunded

Posted in Statistics with tags Albert Einstein, March for Science, Nature, relativity, trumpism on May 8, 2017 by xi'an## round-table on Bayes[ian[ism]]

Posted in Books, pictures, Statistics, University life with tags Bayes factors, Bayesian Analysis, Bayesianism, Bureau international des poids et mesures, decision theory, evidence, France Culture, French book, game theory, Henri Poincaré, neurosciences, non-informative priors, relativity, subjective versus objective Bayes, Université Paris-La Sorbonne on March 7, 2017 by xi'an**I**n a [sort of] coincidence, shortly after writing my review on Le bayésianisme aujourd’hui, I got invited by the book editor, Isabelle Drouet, to take part in a round-table on Bayesianism in La Sorbonne. Which constituted the first seminar in the monthly series of the séminaire “Probabilités, Décision, Incertitude”. Invitation that I accepted and honoured by taking place in this public debate (if not dispute) on all [or most] things Bayes. Along with Paul Egré (CNRS, Institut Jean Nicod) and Pascal Pernot (CNRS, Laboratoire de chimie physique). And without a neuroscientist, who could not or would not attend.

While nothing earthshaking came out of the seminar, and certainly not from me!, it was interesting to hear of the perspectives of my philosophy+psychology and chemistry colleagues, the former explaining his path from classical to Bayesian testing—while mentioning trying to read the book Statistical rethinking I reviewed a few months ago—and the later the difficulty to teach both colleagues and students the need for an assessment of uncertainty in measurements. And alluding to GUM, developed by the Bureau International des Poids et Mesures I visited last year. I tried to present my relativity viewpoints on the [relative] nature of the prior, to avoid the usual morass of debates on the nature and subjectivity of the prior, tried to explain Bayesian posteriors via ABC, mentioned examples from The Theorem that Would not Die, yet untranslated into French, and expressed reserves about the glorious future of Bayesian statistics as we know it. This seminar was fairly enjoyable, with none of the stress induced by the constraints of a radio-show. Just too bad it did not attract a wider audience!

## le bayésianisme aujourd’hui [book review]

Posted in Books, pictures, Statistics, University life with tags Bayes factors, Bayesian Analysis, Bayesianism, big data, Bureau international des poids et mesures, clinical trials, decision theory, evidence, French book, game theory, Harold Jeffreys, Henri Poincaré, measure theory, neurosciences, non-informative priors, R.A. Fisher, relativity, subjective versus objective Bayes, Université Paris-La Sorbonne on March 4, 2017 by xi'an**I**t is quite rare to see a book published in French about Bayesian statistics and even rarer to find one that connects philosophy of science, foundations of probability, statistics, and applications in neurosciences and artificial intelligence. Le bayésianisme aujourd’hui (Bayesianism today) was edited by Isabelle Drouet, a Reader in Philosophy at La Sorbonne. And includes a chapter of mine on the basics of Bayesian inference (à la Bayesian Choice), written in French like the rest of the book.

The title of the book is rather surprising (to me) as I had never heard the term Bayesianism mentioned before. As shown by this link, the term apparently exists. (Even though I dislike the sound of it!) The notion is one of a probabilistic structure of knowledge and learning, à la Poincaré. As described in the beginning of the book. But I fear the arguments minimising the subjectivity of the Bayesian approach should not be advanced, following my new stance on the relativity of probabilistic statements, if only because they are defensive and open the path all too easily to counterarguments. Similarly, the argument according to which the “Big Data” era makesp the impact of the prior negligible and paradoxically justifies the use of Bayesian methods is limited to the case of little Big Data, i.e., when the observations are more or less iid with a limited number of parameters. Not when the number of parameters explodes. Another set of arguments that I find both more modern and compelling [for being modern is not necessarily a plus!] is the ease with which the Bayesian framework allows for integrative and cooperative learning. Along with its ultimate modularity, since each component of the learning mechanism can be extracted and replaced with an alternative. Continue reading