**D**iego Salmerón and Juan Antonio Cano from Murcia, Spain *(check the movie linked to the above photograph!)*, kindly included me in their recent integral prior paper, even though I mainly provided (constructive) criticism. The paper has just been arXived.

**A** few years ago (2008 to be precise), we wrote together an integral prior paper, published in * TEST*, where we exploited the implicit equation defining those priors (Pérez and Berger, 2002), to construct a Markov chain providing simulations from both integral priors. This time, we consider the case of a binomial regression model and the problem of variable selection. The integral equations are similarly defined and a Markov chain can again be used to simulate from the integral priors. However, the difficulty therein follows from the regression structure, which makes selecting training datasets more elaborate, and whose posterior is not standard. Most fortunately, because the training dataset is exactly the right dimension, a re-parameterisation allows for a simulation of Bernoulli probabilities, provided a Jeffreys prior is used on those. (This obviously makes the “prior” dependent on the selected training dataset, but it should not overly impact the resulting inference.)

## Comments for València 9

Posted in Statistics, University life with tags Benidorm, comments, discussions, simulations, Valencia 9 on June 23, 2010 by xi'anFollowing discussions at CREST, we have contributed comments on the following papersBernardo, José M.(Universitat de València, Spain)Integrated objective Bayesian estimation and hypothesis testing.[discussion]Consonni, Guido(Università di Pavia, Italy)On moment priors for Bayesian model choice with applications to directed acyclic graphs.[discussion]Frühwirth-Schnatter, Sylvia(Johannes Kepler Universität Linz, Austria)Bayesian variable selection for random intercept modeling of Gaussian and non-Gaussian data.[discussion]Huber, Mark(Claremont McKenna College, USA)Using TPA for Bayesian inference.[discussion]Lopes, Hedibert(University of Chicago, USA)Particle learning for sequential Bayesian computation.[discussion]Polson, Nicholas(University of Chicago, USA)Shrink globally, act locally: Sparse Bayesian regularization and prediction.[discussion]Wilkinson, Darren(University of Newcastle, UK)Parameter inference for stochastic kinetic models of bacterial gene regulation: a Bayesian approach to systems biology.[discussion](with a possible incoming update on Mark Huber’s comments if we manage to get the simulations running in due time).

## Share:

9 Comments »