**T**oday I am taking part in a one-day workshop at the Université of Clermont Auvergne on ABC. With applications to cosmostatistics, along with Martin Kilbinger [with whom I worked on PMC schemes], Florent Leclerc and Grégoire Aufort. This should prove a most exciting day! (With not enough time to run up Puy de Dôme in the morning, though.)

## Archive for cosmostats

## ABC in Clermont-Ferrand

Posted in Mountains, pictures, Statistics, Travel, University life with tags ABC, ABC-Gibbs, Approximate Bayesian computation, Auvergne, Clermont-Ferrand, conditional sufficiency, cosmostats, dimension reduction, Gibbs sampling, likelihood-free methods, PMC, volcano on September 20, 2019 by xi'an## simulated summary statistics [in the sky]

Posted in Statistics with tags ABC, approximate likelihood, Bayes factor, computer-simulated model, cosmology, cosmostats, de-biasing, urbi et orbi on October 10, 2018 by xi'an**T**hinking it was related with ABC, although in the end it is not!, I recently read a baffling cosmology paper by Jeffrey and Abdalla. The data **d** there means an observed (summary) statistic, while the summary statistic is a transform of the parameter, μ(θ), which calibrates the distribution of the data. With nuisance parameters. More intriguing to me is the sentence that the correct likelihood of **d** is indexed by a simulated version of μ(θ), μ'(θ), rather than by μ(θ). Which seems to assume that the pseudo- or simulated data can be produced for the same value of the parameter as the observed data. The rest of the paper remains incomprehensible for I do not understand how the simulated versions are simulated.

“…the corrected likelihood is more than a factor of exp(30) more probable than the uncorrected. This is further validation of the corrected likelihood; the model (i.e. the corrected likelihood) shows a better goodness-of-fit.”

The authors further ressort to Bayes factors to compare corrected and uncorrected versions of the likelihoods, which leads (see quote) to picking the corrected version. But are they comparable as such, given that the corrected version involves simulations that are treated as supplementary data? As noted by the authors, the Bayes factor unsurprisingly goes to one as the number M of simulations grows to infinity, as supported by the graph below.

## an interesting identity

Posted in Books, pictures, Statistics, University life with tags cosmostats, cross validated, IAP, Institut d'Astrophysique de Paris, Monte Carlo approximations, Monte Carlo Statistical Methods, Pierre Simon Laplace, Rao-Blackwellisation, textbooks on March 1, 2018 by xi'an**A**nother interesting **X** validated question, another remembrance of past discussions on that issue. Discussions that took place in the Institut d’Astrophysique de Paris, nearby this painting of Laplace, when working on our cosmostats project. Namely the potential appeal of recycling multidimensional simulations by permuting the individual components in nearly independent settings. As shown by the variance decomposition in my answer, when opposing N iid pairs (X,Y) to the N combinations of √N simulations of X and √N simulations of Y, the comparison

unsurprisingly gives the upper hand to the iid sequence. A sort of converse to Rao-Blackwellisation…. Unless the production of N simulations gets much more costly when compared with the N function evaluations. No wonder we never see this proposal in Monte Carlo textbooks!