Fw: **29/06/09 - 15:30h** Ciclo de Palestras Pós Graduação em Estatística UFRJ

["Dani Gamerman" <dani@im.ufrj.br>]

---------- Forwarded Message -----------
From: "Alexandra M. Schmidt" <alex@im.ufrj.br>

Caros,

Dando continuidade ao Ciclo de Palestras do Programa de Pós-Graduação em
Estatística do IM-UFRJ, nesta 2a feira, 29/06/09, as 15:30h, teremos a 
palestra do

Ralph S. Silva (Sydney)
Título: A copula based approach to adaptive sampling

O resumo segue abaixo.

Contamos com a presenca de voces.

Acompanhem a atualizacao do programa do nosso ciclo de palestras  no sitio
www.dme.ufrj.br opcao Atividades subopcao Ciclo de Palestras.

Atenciosamente,

c/c Alexandra

Ps.: Desculpem-me pela eventual duplicação da postagem desta mensagem.

Título: A copula based approach to adaptive sampling

Resumo
Our article is concerned with adaptive sampling schemes for Bayesian 
inference that update the proposal densities using previous iterates. We 
introduce a copula based proposal density which is made more efficient by 
combining it with antithetic variable sampling. We compare the copula based 
proposal to an adaptive proposal density based on a multivariate mixture of 
normals and an adaptive random walk Metropolis proposal. We also introduce a 
refinement of the random walk proposal which performs better for multimodal 
target distributions. We compare the sampling schemes using challenging but 
realistic models and priors applied to real data examples. The results show 
that for the examples studied, the adaptive independent Metropolis-Hastings 
proposals are much more efficient than the adaptive random walk proposals 
and that in general the copula based proposal has the best acceptance rates 
and lowest inefficiencies. 
This is joint work with Robert Kohn, Paolo Giordani and Xiuyan Mun.
------- End of Forwarded Message -------


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Dani Gamerman
Depto. de Métodos Estatísticos 
Instituto de Matemática - UFRJ
Caixa Postal 68530
21945-970 Rio de Janeiro, RJ
tel (21) 2562 7911
fax (21) 2562 7374
dani@im.ufrj.br
http://dme.ufrj.br/dani
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