Archive for the pictures Category

Sens’o risotto

Posted in Kids, pictures, Wines with tags , , , , , , on February 6, 2016 by xi'an

A chance dinner last Sunday led us to dine in Sens’o, an empty Italian restaurant on one of the Paris islands with a superlative scallops risotto and a chance encounter resulted from talking from the idle waitress who happened to be a free-lance journalist. We talked for quite a while on her previous trips to Haïti, Egypt, and field hospitals at the Syrian border in Turkey. (The restaurant was empty for a combination of reasons, from the drop in tourists after the November 13 killings to January being a low tide month, to a blistery Sunday night being unattractive for revellers. Not because of bad reviews…)

It’s the selection’s fault not the p-values’… [seminar]

Posted in pictures, Statistics, University life with tags , , , , , , on February 5, 2016 by xi'an

Paris and la Seine, from Pont du Garigliano, Oct. 20, 2011Yoav Benjamini will give a seminar talk in Paris next Monday on the above (full title: “The replicability crisis in science: It’s the selection’s fault not the p-values’“). (That I will miss for being in Warwick at the time.) With a fairly terse abstract:

I shall discuss the problem of lack of replicability of results in science, and point at selective inference as a statistical root cause. I shall then present a few strategies for addressing selective inference, and their application in genomics, brain research and earlier phases of clinical trials where both primary and secondary endpoints are being used.

Details: February 8, 2016, 16h, Université Pierre & Marie Curie, campus Jussieu, salle 15-16-101.

Eagle and Child

Posted in Books, Kids, pictures, Travel, University life, Wines with tags , , , , , , on February 4, 2016 by xi'an

a glacial PhD in Iceland [job announcement]

Posted in Kids, Mountains, pictures, Statistics, Travel, University life with tags , , , , , , on February 3, 2016 by xi'an

[Here is a PhD offer at the University of Iceland that may be of interest to some readers or their students. I would have been interested 30 years ago!]

The Department of Mathematics at the University of Iceland (UI) seeks applicants for a fully funded 3 year PhD position for the project Statistical Models for Glaciology.

The student will develop Bayesian hierarchical spatio-temporal models to the field of glaciology, working with a consortium of experts at the University of Iceland, the University of Missouri and the Norwegian University of Science and Technology. The key people in the consortium are Prof. Birgir Hrafnkelsson at UI, Prof. Chris Wikle, and Prof. Håvard Rue, experts in spatial statistics and Bayesian computation. Another key person is Prof. Gudfinna Adalgeirsdottir at UI, an expect in glaciology. The Glaciology group at UI possesses extensive data and knowledge about the Icelandic glaciers.

The application deadline is February 29, 2016.

Detailed project description

Job ad with information on how to apply:

expectation-propagation from Les Houches

Posted in Books, Mountains, pictures, Statistics, University life with tags , , , , , , , , , , on February 3, 2016 by xi'an

ridge6As CHANCE book editor, I received the other day from Oxford University Press acts from an École de Physique des Houches on Statistical Physics, Optimisation, Inference, and Message-Passing Algorithms that took place there in September 30 – October 11, 2013.  While it is mostly unrelated with Statistics, and since Igor Caron already reviewed the book a year and more ago, I skimmed through the few chapters connected to my interest, from Devavrat Shah’s chapter on graphical models and belief propagation, to Andrea Montanari‘s denoising and sparse regression, including LASSO, and only read in some detail Manfred Opper’s expectation propagation chapter. This paper made me realise (or re-realise as I had presumably forgotten an earlier explanation!) that expectation propagation can be seen as a sort of variational approximation that produces by a sequence of iterations the distribution within a certain parametric (exponential) family that is the closest to the distribution of interest. By writing the Kullback-Leibler divergence the opposite way from the usual variational approximation, the solution equates the expectation of the natural sufficient statistic under both models… Another interesting aspect of this chapter is the connection with estimating normalising constants. (I noticed a slight typo on p.269 in the final form of the Kullback approximation q() to p().

Oxford snapshot

Posted in pictures, Running, Travel, University life with tags , , , on February 2, 2016 by xi'an

Le Monde puzzle [#947]

Posted in Books, Kids, pictures, R, Statistics with tags , , , , , , , on February 2, 2016 by xi'an

Another boardgame in Le Monde mathematical puzzle :

Given an 8×8 chequerboard,  consider placing 2×2 tiles over this chequerboard until (a) the entire surface is covered and (b) removing a single 2×2 tile exposes some of the original chequerboard. What is the maximal number of 2×2 tiles one can set according to this scheme? And for a 10×10 chequerboard?

This puzzle reminded me of Wilfrid Kendall’s introduction to perfect sampling  with leaves seen through a ceiling window falling from above, until sky was no longer visible. The representation was used by Wilfrid to explain that coupling from the past did not need to go all the way back to infinity:

Defining a random coverage of the chequerboard by those 2×2 tiles amounts to selecting a random permutation þ of 1:(n-1)² and finding the subvector of þ producing a full coverage

 grid=matrix(0,n,n)
 path=sample(1:(n-1)^2) #random permutation
 path=path+((path-1)%/%(n-1)) #account for border shift
 i=1
 neigh=c(0,1,n,n+1)
 while (min(grid)==0){ #all entries covered
   grid[path[i]+neigh]=grid[path[i]+neigh]+1
   i=i+1
 }
 i=i-1

Then removing superfluous tiles:

for (k in sample(1:i)){
 krid=grid
 krid[path[k]+neigh]=krid[path[k]+neigh]-1
 if (min(krid)>0){ #off used below
   off[k]=FALSE; grid=krid} #useless tile
 }

And checking the remaining ones are truly essential:

mingrid=0
for (k in (1:i)[off]){
 krid=grid
 krid[path[k]+neigh]=krid[path[k]+neigh]-1
 mingrid=max(mingrid,min(krid))
 }
sky=(mingrid>0) #rejection of the grid

leads to the maximum number of tiles to be [at least] M=16,25,37,54 for n=6,8,10,12…

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