unbiased estimation of log-normalising constants

Maxime Rischard, Pierre Jacob, and Natesh Pillai [warning: both of whom are co-authors and friends of mine!] have just arXived a paper on the use of path sampling (a.k.a., thermodynamic integration) for log-constant unbiased approximation and the resulting consequences on Bayesian model comparison by X validation. If the goal is the estimation of the log […]

normalising constants of G-Wishart densities

Abdolreza Mohammadi, Hélène Massam, and Gérard Letac arXived last week a paper on a new approximation of the ratio of two normalising constants associated with two G-Wishart densities associated with different graphs G. The G-Wishart is the generalisation of the Wishart distribution by Alberto Roverato to the case when some entries of the matrix are […]

estimating normalising constants [mea culpa?!]

“The basic idea is to estimate the parameters by learning to discriminate between the data x and some artificially generated noise y.” In the sequel of this popular earlier post of mine on [not] estimating normalising constants, Simon Barthelmé and Nicolas Chopin pointed me to recent papers by Michael Gutmann and Aapo Hyvaärinen on this […]

where did the normalising constants go?! [part 2]

Coming (swiftly and smoothly) back home after this wonderful and intense week in Banff, I hugged my loved ones,  quickly unpacked, ran a washing machine, and  then sat down to check where and how my reasoning was wrong. To start with, I experimented with a toy example in R: and (of course!) it produced the […]

where did the normalising constants go?! [part 1]

When listening this week to several talks in Banff handling large datasets or complex likelihoods by parallelisation, splitting the posterior as and handling each term of this product on a separate processor or thread as proportional to a probability density, then producing simulations from the mi‘s and attempting at deriving simulations from the original product, […]