## LGM 2012, Trondheim

Posted in Mountains, pictures, Running, Statistics, Travel, University life with tags , , , , , on May 31, 2012 by xi'an

A break from the “snapshots from Guérande” that will be a relief for all ‘ Og readers, I am sure: I am now in Trondheim, Norway, for the second Latent Gaussian model meeting, organised by Håvard Rue and his collaborators. As in the earlier edition in Zürich, the main approach to those models (that is adopted in the talks) is the INLA methodology of Rue, Martino and Chopin. I nonetheless (given the theme) gave a presentation on Rao-Blackwellisation techniques for MCMC algorithms. As I had not printed the program of the meeting prior to my departure (blame Guérande!), I had not realised I had only 20 minutes for my talk and kept adding remarks and slides during the flight from Amsterdam to Trondheim [where the clouds prevented me from seeing Jotunheimen]. (So I had to cut the second half of the talk below on parallelisation. Even with this cut, the 20 minutes went awfully fast!) Apart from my talk, I am afraid I was not in a sufficient state of awareness [due to a really early start] to give a comprehensive of the afternoon talks….

Trondheim is a nice city that sometimes feels like a village despite its size. Walking up to the university along typical wooden houses, then going around the town and along the river tonight while running a 10k loop left me with the impression of a very pleasant place (at least in the summer months).

## snapshot from Guérande (4)

Posted in pictures, Travel with tags , , , , on May 30, 2012 by xi'an

## the universe in zero words

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , , , , , , , , , on May 30, 2012 by xi'an

The universe in zero words: The story of mathematics as told through equations is a book with a very nice cover: in case you cannot see the details on the picture, what looks like stars on a bright night sky are actually equations discussed in the book (plus actual stars!)…

## snapshot from Guérande (3)

Posted in pictures, Running, Travel with tags , , , , , on May 29, 2012 by xi'an

## optimal direction Gibbs

Posted in Statistics, University life with tags , , , , , , on May 29, 2012 by xi'an

An interesting paper appeared on arXiv today. Entitled On optimal direction gibbs sampling, by Andrés Christen, Colin Fox, Diego Andrés Pérez-Ruiz and Mario Santana-Cibrian, it defines optimality as picking the direction that brings the maximum independence between two successive realisations in the Gibbs sampler. More precisely, it aims at choosing the direction e that minimises the mutual information criterion

$\int\int f_{Y,X}(y,x)\log\dfrac{f_{Y,X}(y,x)}{f_Y(y)f_X(x)}\,\text{d}x\,\text{d}y$

I have a bit of an issue about this choice because it clashes with measure theory. Indeed, in one Gibbs step associated with e the transition kernel is defined in terms of the Lebesgue measure over the line induced by e. Hence the joint density of the pair of successive realisations is defined in terms of the product of the Lebesgue measure on the overall space and of the Lebesgue measure over the line induced by e… While the product in the denominator is defined against the product of the Lebesgue measure on the overall space and itself. The two densities are therefore not comparable since not defined against equivalent measures… The difference between numerator and denominator is actually clearly expressed in the normal example (page 3) when the chain operates over a n dimensional space, but where the conditional distribution of the next realisation is one-dimensional, thus does not relate with the multivariate normal target on the denominator. I therefore do not agree with the derivation of the mutual information henceforth produced as (3).

The above difficulty is indirectly perceived by the authors, who note “we cannot simply choose the best direction: the resulting Gibbs sampler would not be irreducible” (page 5), an objection I had from an earlier page… They instead pick directions at random over the unit sphere and (for the normal case) suggest using a density over those directions such that

$h^*(\mathbf{e})\propto(\mathbf{e}^\prime A\mathbf{e})^{1/2}$

which cannot truly be called “optimal”.

More globally, searching for “optimal” directions (or more generally transforms) is quite a worthwhile idea, esp. when linked with adaptive strategies…