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)

Invited Talks:

 Name Title Abstract Basu, S Double Dirichlet process mixtures Abstract Cox, D Useful priors for covariance operators Abstract Ghoshal, SG Dirichlet process, related priors and posterior asymptotics Abstract Guglielmi, A Semiparametric inference for the accelerated failure time model using hierarchical mixtures with generalized gamma processes Abstract Herring, A Semiparametric bayes joint modeling with functional predictors Abstract Ho, MW A Bayes method for a Monotone hazard rate via $\mathbf{S}$-paths Abstract James, L Some new identities for Dirichlet means and implications Abstract Johnson, W Semi-parametric survival analysis with time dependent covariates Abstract Karabatsos, G Bayesian Nonparametric Single-Index Regression Abstract Kim, Y Posterior consisteny of logistic random effect models Abstract Kokolakis, G Convexification and Multimodality of Random Probability Measures Abstract Laud, P Genetic association studies in the presence of population structure and admixture Abstract Lee, J Posterior consistency of species sampling priors Abstract Nieto-Barajas, LE Some good news about nonparametric priors in density estimation Abstract Petrone, S Hybrid Dirichlet processes for functional data Abstract Popova, E Bayesian semiparametric analysis for a single item maintenance optimisation Abstract Ruggiero, M Bayesian countable representation of some population genetics diffusions Abstract Simoni, A Regularised posteriors in linear ill-posed inverse problems Abstract Spano, D Canonical representations for dependent Dirichlet populations Abstract Steel, M Bayesian nonparametric modelling with the Dirichlet process regression smoother Abstract