When playing with Peter Rossi’s bayesm R package during a visit of Jean-Michel Marin to Paris, last week, we came up with the above Gibbs outcome. The setting is a Gaussian mixture model with three components in dimension 5 and the prior distributions are standard conjugate. In this case, with 500 observations and 5000 Gibbs iterations, the Markov chain (for one component of one mean of the mixture) has two highly distinct regimes: one that revolves around the true value of the parameter, 2.5, and one that explores a much broader area (which is associated with a much smaller value of the component weight). What we found amazing is the Gibbs ability to entertain both regimes, simultaneously.
Archive for Jean-Michel Marin
Our book is nearly out..! The Springer webpage is ready, we have sent the proofs back, amazon
is missing has now included the above picture, things are moving towards the publication date, supposed to be November 30. Just in time for Christmas! And not too early given that we packed off in early February…
Jean-Michel Marin visited me in Paris last week and, besides taking part in Pierre’s PhD defence, we made enough progress to close two more chapters of the new edition of Bayesian Core (soon to be Bayesian Essentials with R!) This follows the good work session we had in Carnon where we also completed two chapters (although it was hard to convince anyone that renting a flat by the Mediterranean sea was at all connected with…work! While it was: the only breaks I took were my morning runs…). There just remains one single chapter to complete, now, the one on hierarchical Bayes models. By all means, I dearly want to see it done by November 1!!!