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An Isaac Newton Institute Workshop


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 | Contributed Talks | Accepted Posters | Presentations on the web | Photograph

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

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