Archive for Bayesian Methods in Cosmology

Astrostatistics school

Posted in Mountains, pictures, R, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , , , , on October 17, 2017 by xi'an

What a wonderful week at the Astrostat [Indian] summer school in Autrans! The setting was superb, on the high Vercors plateau overlooking both Grenoble [north] and Valence [west], with the colours of the Fall at their brightest on the foliage of the forests rising on both sides of the valley and a perfect green on the fields at the centre, with sun all along, sharp mornings and warm afternoons worthy of a late Indian summer, too many running trails [turning into X country ski trails in the Winter] to contemplate for a single week [even with three hours of running over two days], many climbing sites on the numerous chalk cliffs all around [but a single afternoon for that, more later in another post!]. And of course a group of participants eager to learn about Bayesian methodology and computational algorithms, from diverse [astronomy, cosmology and more] backgrounds, trainings and countries. I was surprised at the dedication of the participants travelling all the way from Chile, Péru, and Hong Kong for the sole purpose of attending the school. David van Dyk gave the first part of the school on Bayesian concepts and MCMC methods, Roberto Trotta the second part on Bayesian model choice and hierarchical models, and myself a third part on, surprise, surprise!, approximate Bayesian computation. Plus practicals on R.

As it happens Roberto had to cancel his participation and I turned for a session into Christian Roberto, presenting his slides in the most objective possible fashion!, as a significant part covered nested sampling and Savage-Dickey ratios, not exactly my favourites for estimating constants. David joked that he was considering postponing his flight to see me talk about these, but I hope I refrained from engaging into controversy and criticisms… If anything because this was not of interest for the participants. Indeed when I started presenting ABC through what I thought was a pedestrian example, namely Rasmus Baath’s socks, I found that the main concern was not running an MCMC sampler or a substitute ABC algorithm but rather an healthy questioning of the construction of the informative prior in that artificial setting, which made me quite glad I had planned to cover this example rather than an advanced model [as, e.g., one of those covered in the packages abc, abctools, or abcrf]. Because it generated those questions about the prior [why a Negative Binomial? why these hyperparameters? &tc.] and showed how programming ABC turned into a difficult exercise even in this toy setting. And while I wanted to give my usual warning about ABC model choice and argue for random forests as a summary selection tool, I feel I should have focussed instead on another example, as this exercise brings out so clearly the conceptual difficulties with what is taught. Making me quite sorry I had to leave one day earlier. [As did missing an extra run!] Coming back by train through the sunny and grape-covered slopes of Burgundy hills was an extra reward [and no one in the train commented about the local cheese travelling in my bag!]

 

marginal likelihoods from MCMC

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , on April 26, 2017 by xi'an

A new arXiv entry on ways to approximate marginal likelihoods based on MCMC output, by astronomers (apparently). With an application to the 2015 Planck satellite analysis of cosmic microwave background radiation data, which reminded me of our joint work with the cosmologists of the Paris Institut d’Astrophysique ten years ago. In the literature review, the authors miss several surveys on the approximation of those marginals, including our San Antonio chapter, on Bayes factors approximations, but mention our ABC survey somewhat inappropriately since it is not advocating the use of ABC for such a purpose. (They mention as well variational Bayes approximations, INLA, powered likelihoods, if not nested sampling.)

The proposal of this paper is to identify the marginal m [actually denoted a there] as the normalising constant of an unnormalised posterior density. And to do so the authors estimate the posterior by a non-parametric approach, namely a k-nearest-neighbour estimate. With the additional twist of producing a sort of Bayesian posterior on the constant m. [And the unusual notion of number density, used for the unnormalised posterior.] The Bayesian estimation of m relies on a Poisson sampling assumption on the k-nearest neighbour distribution. (Sort of, since k is actually fixed, not random.)

If the above sounds confusing and imprecise it is because I am myself rather mystified by the whole approach and find it difficult to see the point in this alternative. The Bayesian numerics does not seem to have other purposes than producing a MAP estimate. And using a non-parametric density estimate opens a Pandora box of difficulties, the most obvious one being the curse of dimension(ality). This reminded me of the commented paper of Delyon and Portier where they achieve super-efficient convergence when using a kernel estimator, but with a considerable cost and a similar sensitivity to dimension.

Bayesian methods in cosmology

Posted in Statistics with tags , , , , , , , , , , , , on January 18, 2017 by xi'an

A rather massive document was arXived a few days ago by Roberto Trotta on Bayesian methods for cosmology, in conjunction with an earlier winter school, the 44th Saas Fee Advanced Course on Astronomy and Astrophysics, “Cosmology with wide-field surveys”. While I never had the opportunity to give a winter school in Saas Fee, I will give next month a course on ABC to statistics graduates in another Swiss dream location, Les Diablerets.  And next Fall a course on ABC again but to astronomers and cosmologists, in Autrans, near Grenoble.

The course document is an 80 pages introduction to probability and statistics, in particular Bayesian inference and Bayesian model choice. Including exercises and references. As such, it is rather standard in that the material could be found as well in textbooks. Statistics textbooks.

When introducing the Bayesian perspective, Roberto Trotta advances several arguments in favour of this approach. The first one is that it is generally easier to follow a Bayesian approach when compared with seeking a non-Bayesian one, while recovering long-term properties. (Although there are inconsistent Bayesian settings.) The second one is that a Bayesian modelling allows to handle naturally nuisance parameters, because there are essentially no nuisance parameters. (Even though preventing small world modelling may lead to difficulties as in the Robbins-Wasserman paradox.) The following two reasons are the incorporation of prior information and the appeal on conditioning on the actual data.

trottaThe document also includes this above and nice illustration of the concentration of measure as the dimension of the parameter increases. (Although one should not over-interpret it. The concentration does not occur in the same way for a normal distribution for instance.) It further spends quite some space on the Bayes factor, its scaling as a natural Occam’s razor,  and the comparison with p-values, before (unsurprisingly) introducing nested sampling. And the Savage-Dickey ratio. The conclusion of this model choice section proposes some open problems, with a rather unorthodox—in the Bayesian sense—line on the justification of priors and the notion of a “correct” prior (yeech!), plus an musing about adopting a loss function, with which I quite agree.