An Isaac Newton Institute Workshop

CONSTRUCTION AND PROPERTIES OF BAYESIAN NONPARAMETRIC REGRESSION MODELS

6 August to 10 August 2007

Organisers: Professor Nils Hjort (Oslo), Dr Chris Holmes (Oxford), Professor Peter Müller (Texas) and Professor Stephen Walker (Canterbury)

in association with the Newton Institute programme Bayesian Nonparametric Regression: Theory, Methods and Applications (30 July to 24 August 2007)

Programme | Participants | Application | Workshop Home Page | Invited Talks | Contributed Talks | Presentations on the web | Photograph

Accepted Posters:

Name Title Abstract Poster
Archambeau, C Approximate Bayesian smoother for diffusion processes Abstract
Bochkina, N A priori regularity properties of nonparametric function estimators Abstract
Caron, F Stationary Pitman-Yor processes Abstract
Cerquetti, A Some results on Bayesian nonparametric priors derived from Poisson-Kingman models Abstract Poster
Coyle, D Improving the separability of multiple feature types for a BCI by neural time-series prediction preprocessing Abstract
Kolossiatis, M Bayesian Nonparametric Modelling of Grouped Data Abstract
Krnjajic, M Bayesian nonparametric modeling in quantile regression Abstract Poster
Martinelli, A Large deviation principle for mixtures of Dirichlet processes Abstract
Martinez-Ovando, J-C Nonparametric Bayesian modelling of Markov processes Abstract
Shi, JQ Gaussian Process Functional Regression Modelling for Batch Data Abstract
Tokdar, ST A Bayesian implementation of sufficient dimension reduction in regression Abstract

Bayesian Nonparametric Regression: Theory, Methods and Applications | Workshops | Newton Institute Home Page | pdf version of poster