Archive for Vercors
[Astrostat summer school] sunrise [jatp]
Posted in Statistics with tags astrostatistics, Autrans, Belledone, Fall, fog, French Alps, Grande Chartreuse, Grenoble, jatp, summer school, sunrise, Vercors on October 10, 2017 by xi'an
[summer Astrostat school] room with a view [jatp]
Posted in Mountains, pictures, R, Running, Statistics, Travel, University life with tags astrostatistics, Aussois, Banff, Bayesian statistics, France, Grenoble, Les Diablerets, R, RStudio, summer school, Vercors on October 9, 2017 by xi'an
I just arrived in Autrans, on the Plateau du Vercors overlooking Grenoble and the view is fabulistic! Trees have started to turn red and yellow, the weather is very mild, and my duties are restricted to teaching ABC to a group of enthusiastic astronomers and cosmologists..! Second advanced course on ABC in the mountains this year, hard to beat (except by a third course). The surroundings are so serene and peaceful that I even conceded to install RStudio for my course! Instead of sticking to my favourite vim editor and line commands.
morning run [jatp]
Posted in Kids, Mountains, pictures, Running, Travel, Wines with tags birthday, Drôme, France, jatp, morning light, morning run, Rhone-Alpes, trail running, Vercors on April 30, 2017 by xi'an
Bayesian methods in cosmology
Posted in Statistics with tags ABC, astrostatistics, Autrans, Bayesian Methods in Cosmology, Grenoble, Les Diablerets, nested sampling, Ockham's razor, Pierre Simon de Laplace, Roberto Trotta, Saas Fee, Switzerland, Vercors 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.
The 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.
