Bayes in Grenoble: Bernardo Nipoti

on the February 4, 2019

February, 4th at 11:00 am
Bernardo Nipoti (Trinity College Dublin) will talk about "On the Pitman-Yor process with spike and slab base measure ". The event will take place on February, 4 at 11:00 am at Inria.
Bayes in Grenoble is a new reading group on Bayesian statistical methods. The purpose of this group is to gather the Grenoble Bayesian community on a monthly basis around noteworthy papers. Those can equally focus on theory, methods, learning, applications, computations, etc, and can be seminal papers as well as recent preprints, as soon as they relate to Bayes.

The sessions last two hours: the presentation is followed by an informal moment where participants will enjoy cocktails and snacks offered by the Grenoble Alpes Data Institute.

The reading group is organised by Julyan Arbel and Florence Forbes. Feel free to contact them if you wish to attend/be added to the mailing list and/or give a talk.

React on social media: #BIGseminar

4 February 2019, Bernardo Nipoti (Trinity College Dublin)

On the Pitman-Yor process with spike and slab base measure


For the most popular discrete nonparametric models, beyond the Dirichlet process, the prior guess at the shape of the data-generating distribution, also known as the base measure, is assumed to be diffuse. Such a specification greatly simplifies the derivation of analytical results, allowing for a straightforward implementation of Bayesian nonparametric inferential procedures. However, in several applied problems the available prior information leads naturally to the incorporation of an atom into the base measure, and then the Dirichlet process is essentially the only tractable choice for the prior. In this paper we fill this gap by considering the Pitman–Yor process with an atom in its base measure. We derive computable expressions for the distribution of the induced random partitions and for the predictive distributions. These findings allow us to devise an effective generalized Pólya urn Gibbs sampler. Applications to density estimation, clustering and curve estimation, with both simulated and real data, serve as an illustration of our results and allow comparisons with existing methodology. In particular, we tackle a functional data analysis problem concerning basal body temperature curves.

Published on January 31, 2019

Practical informations


Inria Montbonnot Saint-Martin
Room F107