Chopin, Nicolas, Gadat, Sébastien, Guedj, Benjamin, Guyader, Arnaud and Vernet, Elodie (2015) On some recent advances in high dimensional Bayesian Statistics. TSE Working Paper, n. 15-557

Warning
There is a more recent version of this item available.
[thumbnail of bayescomp.pdf]
Preview
Text
Download (2MB) | Preview

Abstract

This paper proposes to review some recent developments in Bayesian statistics for high dimensional data. After giving some brief motivations in a short introduction, we describe new advances in the understanding of Bayes posterior computation as well as theoretical contributions in non parametric and high dimensional Bayesian approaches. From an applied point of view, we describe the so-called SQMC particle method to compute posterior Bayesian law, and provide a nonparametric analysis of the widespread ABC method. On the theoretical side, we describe some recent advances in Bayesian consistency for a nonparametric hidden Markov model as well as new PAC-Bayesian results for different models of high dimensional regression.

Item Type: Monograph (Working Paper)
Language: English
Date: February 2015
Subjects: B- ECONOMIE ET FINANCE
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 16 Mar 2015 14:56
Last Modified: 02 Apr 2021 15:49
OAI Identifier: oai:tse-fr.eu:29078
URI: https://publications.ut-capitole.fr/id/eprint/16708

Available Versions of this Item

View Item

Downloads

Downloads per month over past year