Bayesian nonparametric regression has traditionally been studied as two separate disciplines. On the one hand researchers have considered the construction of probability measures on spaces of univariate density functions, which act as prior distributions with full coverage in Bayesian inference. These prior distributions are typically achieved by the construction of stochastic processes; the most well known being the Dirichlet process. On the other hand, researchers in stochastic modelling have investigated the construction of probability measures on general function spaces which act as regression functions in specifying the evolution of parametric distributions indexed on a covariate set, such as the mean function of a Gaussian process. To date there has been surprisingly little overlap between these two strands of research.
The aim of the programme is to bring together researchers working in both areas of Bayesian nonparametrics in order to stimulate further research and discuss the foundations of fully nonparametric regression, in theory, methods and applications.
6 August 2007 to 10 August 2007
20 August 2007 to 21 August 2007
|Monday 30th July 2007|
|16:00 to 18:00||Discussion Room|
|Wednesday 15th August 2007|
|15:45 to 16:45||Room 2|
|Thursday 16th August 2007|
|11:00 to 12:00||
Subhashis Ghoshal North Carolina State University
|15:45 to 16:45||Room 2|
|Friday 17th August 2007|
|11:15 to 12:15||Room 2|
|Tuesday 21st August 2007|
|11:15 to 12:15||
Nils Hjort University of Oslo
|Thursday 23rd August 2007|
|11:15 to 12:15||
Pierpaolo De Blasi
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