EconPapers    
Economics at your fingertips  
 

On some recent advances in high dimensional Bayesian Statistics

Nicolas Chopin, Sébastien Gadat (), Benjamin Guedj, Arnaud Guyader and Elodie Vernet

No 15-557, TSE Working Papers from Toulouse School of Economics (TSE)

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.

New Economics Papers: this item is included in nep-ecm
Date: 2015-02
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://perso.math.univ-toulouse.fr/gadat/files/2012/04/bayescomp.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:tse:wpaper:29078

Access Statistics for this paper

More papers in TSE Working Papers from Toulouse School of Economics (TSE) Contact information at EDIRC.
Bibliographic data for series maintained by ().

 
Page updated 2019-11-06
Handle: RePEc:tse:wpaper:29078