## Archive for Charles Darwin

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , on April 30, 2019 by xi'an

Ziheng Yang and Tianqui Zhu published a paper in PNAS last year that criticises Bayesian posterior probabilities used in the comparison of models under misspecification as “overconfident”. The paper is written from a phylogeneticist point of view, rather than from a statistician’s perspective, as shown by the Editor in charge of the paper [although I thought that, after Steve Fienberg‘s intervention!, a statistician had to be involved in a submission relying on statistics!] a paper , but the analysis is rather problematic, at least seen through my own lenses… With no statistical novelty, apart from looking at the distribution of posterior probabilities in toy examples. The starting argument is that Bayesian model comparison is often reporting posterior probabilities in favour of a particular model that are close or even equal to 1.

“The Bayesian method is widely used to estimate species phylogenies using molecular sequence data. While it has long been noted to produce spuriously high posterior probabilities for trees or clades, the precise reasons for this over confidence are unknown. Here we characterize the behavior of Bayesian model selection when the compared models are misspecified and demonstrate that when the models are nearly equally wrong, the method exhibits unpleasant polarized behaviors,supporting one model with high confidence while rejecting others. This provides an explanation for the empirical observation of spuriously high posterior probabilities in molecular phylogenetics.”

The paper focus on the behaviour of posterior probabilities to strongly support a model against others when the sample size is large enough, “even when” all models are wrong, the argument being apparently that the correct output should be one of equal probability between models, or maybe a uniform distribution of these model probabilities over the probability simplex. Why should it be so?! The construction of the posterior probabilities is based on a meta-model that assumes the generating model to be part of a list of mutually exclusive models. It does not account for cases where “all models are wrong” or cases where “all models are right”. The reported probability is furthermore epistemic, in that it is relative to the measure defined by the prior modelling, not to a promise of a frequentist stabilisation in a ill-defined asymptotia. By which I mean that a 99.3% probability of model M¹ being “true”does not have a universal and objective meaning. (Moderation note: the high polarisation of posterior probabilities was instrumental in our investigation of model choice with ABC tools and in proposing instead error rates in ABC random forests.)

The notion that two models are equally wrong because they are both exactly at the same Kullback-Leibler distance from the generating process (when optimised over the parameter) is such a formal [or cartoonesque] notion that it does not make much sense. There is always one model that is slightly closer and eventually takes over. It is also bizarre that the argument does not account for the complexity of each model and the resulting (Occam’s razor) penalty. Even two models with a single parameter are not necessarily of intrinsic dimension one, as shown by DIC. And thus it is not a surprise if the posterior probability mostly favours one versus the other. In any case, an healthily sceptic approach to Bayesian model choice means looking at the behaviour of the procedure (Bayes factor, posterior probability, posterior predictive, mixture weight, &tc.) under various assumptions (model M¹, M², &tc.) to calibrate the numerical value, rather than taking it at face value. By which I do not mean a frequentist evaluation of this procedure. Actually, it is rather surprising that the authors of the PNAS paper do not jump on the case when the posterior probability of model M¹ say is uniformly distributed, since this would be a perfect setting when the posterior probability is a p-value. (This is also what happens to the bootstrapped version, see the last paragraph of the paper on p.1859, the year Darwin published his Origin of Species.)

## machine learning methods are useful for ABC [or my first PCI Evol Biol!]

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , on November 23, 2017 by xi'an

While I am still working on setting a PCI [peer community in] Comput Stats, having secure sponsorship of some societies (ASA, KSS, RSS, SFdS, and hopefully ISBA), my coauthors Jean-Michel Marin and Louis Raynal submitted our paper ABC random forests for Bayesian parameter inference to PCI Evol Biol. And after a few months of review, including a revision accounting for the reviewers’ requests, our paper stood the test and the recommendation by Michael Blum and Dennis Prangle got published there. Great news, and hopefully helpful for our submission within the coming days!

Posted in Books, Kids, pictures, University life with tags , , , , , , , , , , , , , , , , on September 10, 2016 by xi'an

When in Sacramento two weeks ago I came across the Beers Books Center bookstore, with a large collection of used and (nearly) new cheap books and among other books I bought Greg Bear’s Darwin Radio. I had (rather) enjoyed another book of his’, Hull Zero Three, not to mention one of his first books, Blood Music, I read in the mid 1980’s, and the premises of this novel sounded promising, not mentioning the Nebula award. The theme is of a major biological threat, apparently due to a new virus, and of the scientific unraveling of what the threat really means. (Spoilers alert!) In that respect it sounds rather similar to the (great) Crichton‘s The Andromeda Strain, which is actually mentioned by some characters in this book. As is Ebola, as a sort of contrapoint (since Ebola is a deadly virus, although the epidemic in Western Africa now seems to have vanished). The biological concept exploited here is dormant DNA in non-coding parts of the genome that periodically get awaken and induce massive steps in the evolution. So massive that carriers of those mutations are killed by locals. Until the day it happens in an all-connected World and the mutation can no longer be stopped. The concept is compelling if not completely convincing of course, while the outcome of a new human race, which is to Homo Sapiens what Homo Sapiens was to Neanderthal, is rather disappointing. (How could it be otherwise?!) But I did appreciate the postulate of a massive and immediate change in the genome, even though the details were disputable and the dismissal of Dawkins‘ perspective poorly defended. From a stylistic perspective, the style is at time heavy, while there are too many chance occurrences, like the main character happening to be in Georgia for a business deal (spoilers, spoilers!) at the times of the opening of collective graves, or the second main character coming upon a couple of Neanderthal mummies with a Sapiens baby, or yet this pair of main characters falling in love and delivering a live mutant baby-girl. But I enjoyed reading it between San Francisco and Melbourne, with a few hours of lost sleep and work. It is a page turner, no doubt! I also like the political undercurrents, from riots to emergency measures, to an effective dictatorship controlling pregnancies and detaining newborns and their mothers.

One important thread in the book deals with anthropology digs getting against Native claims to corpses and general opposition to such digs. This reminded me of a very recent article in Nature where a local Indian tribe had claimed rights to several thousand year old skeletons, whose DNA was then showed to be more related with far away groups than the claimants. But where the tribe was still granted the last word, in a rather worrying jurisprudence.

## Philosophy of Science, a very short introduction (and review)

Posted in Books, Kids, Statistics, Travel with tags , , , , , , , , , , , on November 3, 2013 by xi'an

When visiting the bookstore on the campus of the University of Warwick two weeks ago, I spotted this book, Philosophy of Science, a very short introduction, by Samir Okasha, and the “bargain” offer of getting two books for £10 enticed me to buy it along with a Friedrich Nietzsche, a very short introduction… (Maybe with the irrational hope that my daughter would take a look at those for her philosophy course this year!)

Popper’s attempt to show that science can get by without induction does not succeed.” (p.23)

Since this is [unsusrprisingly!] a very short introduction, I did not get much added value from the book. Nonetheless, it was an easy read for short trips in the metro and short waits here and there. And would be a good [very short] introduction to any one newly interested in the philosophy of sciences. The first chapter tries to define what science is, with reference to the authority of Popper (and a mere mention of Wittgenstein), and concludes that there is no clear-cut demarcation between science and pseudo-science. (Mathematics apparently does not constitute a science: “Physics is the most fundamental science of all”, p.55) I would have liked to see the quote from Friedrich Nietzsche

“It is perhaps just dawning on five or six minds that physics, too, is only an interpretation and exegesis of the world (to suit us, if I may say so!) and not a world-explanation.”

in Beyond Good and Evil. as it illustrates the main point of the chapter and maybe the book that scientific theories can never be proven true, Plus, it is often misinterpreted as a anti-science statement by Nietzsche. (Plus, it links both books I bought!) Continue reading

## Darwin day in Paris

Posted in Statistics, University life with tags , , , , on May 10, 2012 by xi'an

Here is the announcement for the second “Journée Darwin“, which will take place on Friday, May the 11th [keep their tusks!], in Chimie ParisTech (near Institut Henri Poincaré),  Amphithéâtre Friedel, starting at 9h30:

The “Journées Darwin” are a series of meetings aimed at bringing together researchers in the Parisian basin working on biological evolution. Each “Journée” consists in a small number of seminars, in which the speakers are expected to explain the philosophy and the perspectives motivating their research, focusing on long-range goals rather than on immediate results. The goal is to help in establishing connections among researchers interested in different aspects of biological evolution, working on different systems and in different laboratories.

## genetics

Posted in Books, Kids, Travel, University life with tags , , , , , , , , , , on April 9, 2012 by xi'an

Today, I was reading in the science leaflet of Le Monde about a new magnitude in sequencing cancerous tumors (wrong link, I know…). This made me wonder whether the sequence of (hundreds of) mutations leading from a normal cell to a cancerous one could be reconstituted in the way a genealogy is. (This reminds me of another exciting genetic article I read in the Eurostar back from London on Thursday, in the Economist, about the colonization of Madagascar by 30 women from the Malay archipelago: “The island was one of the last places on Earth to be settled, receiving its earliest migrants in the middle of the first millennium AD…“)

As a double coincidence, I was reading La Recherche yesterday in the métro to Dauphine, which central theme this month is about heredity beyond genetics. (Double because this also connected with the meeting in London.) The keyword is epigenetics, namely the activation or inactivation of a gene and the hereditary transmission of this character w/o a genetic mutation. This is quite interesting as it implies the hereditability of some adopted traits, i.e. forces one to reconsider the nature versus nurture debate. (This sentence is another input due to Galton!) It also implies that a much faster rate of species differentiation due to environmental changes (than the purely genetic one) is possible, which may sound promising in the light of the fast climate changes we are currently facing. However, what I do not understand is why the journal included a paper on the consequences of epigenetics on the Darwinian theory of evolution and… intelligent design. Indeed, I do not see why the inclusion of different vectors in the hereditary process would contradict Darwin’s notion of natural selection. Or even why considering a scientific modification or replacement of the current Darwinian theory of evolution would be an issue. Charles Darwin wrote his book in 1859, prior to the start of genetics, and the immense advances made since then led to modifications and adjustments from his original views. Without involving any irrational belief in the process.

## Galton & simulation

Posted in Books, R, Statistics with tags , , , , , , , , on September 28, 2010 by xi'an

Stephen Stigler has written a paper in the Journal of the Royal Statistical Society Series A on Francis Galton’s analysis of (his cousin) Charles Darwin’ Origin of Species, leading to nothing less than Bayesian analysis and accept-reject algorithms!

“On September 10th, 1885, Francis Galton ushered in a new era of Statistical Enlightenment with an address to the British Association for the Advancement of Science in Aberdeen. In the process of solving a puzzle that had lain dormant in Darwin’s Origin of Species, Galton introduced multivariate analysis and paved the way towards modern Bayesian statistics. The background to this work is recounted, including the recognition of a failed attempt by Galton in 1877 as providing the first use of a rejection sampling algorithm for the simulation of a posterior distribution, and the first appearance of a proper Bayesian analysis for the normal distribution.”

The point of interest is that Galton proposes through his (multi-stage) quincunx apparatus a way to simulate from the posterior of a normal mean (here is an R link to the original quincunx). This quincunx has a vertical screen at the second level that acts as a way to physically incorporate the likelihood (it also translates the fact that the likelihood is in another “orthogonal” space, compared  with the prior!):

“Take another look at Galton’s discarded 1877 model for natural selection (Fig. 6). It is nothing less that a workable simulation algorithm for taking a normal prior (the top level) and a normal likelihood (the natural selection vertical screen) and finding a normal posterior (the lower level, including the rescaling as a probability density with the thin front compartment of uniform thickness).”

Besides a simulation machinery (steampunk Monte Carlo?!), it also offers the enormous appeal of proposing the derivation of the normal-normal posterior for the very first time:

“Galton was not thinking in explicit Bayesian terms, of course, but mathematically he has posterior $\mathcal{N}(0,C_2)\propto\mathcal{N}(0,A_2)\times f(x=0|y)$. This may be the earliest appearance of this calculation; the now standard derivation of a posterior distribution in a normal setting with a proper normal prior. Galton gave the general version of this result as part of his 1885 development, but the 1877 version can be seen as an algorithm employing rejection sampling that could be used for the generation of values from a posterior distribution. If we replace $f(x)$ above by the density $\mathcal{N}(a,B_2)$, his algorithm would generate the posterior distribution of Y given X=a, namely $\mathcal{N}(aC_2/B_2, C_2)$. The assumption of normality is of course needed for the particular formulae here, but as an algorithm the normality is not essential; posterior values for any prior and any location parameter likelihood could in principle be generated by extending this algorithm.” Continue reading