Fifth Workshop on
Valencia (Spain), June 14-16, 2007


Invited talks

Jim Berger (Duke University) Analysis of Complex Computer Models of Processes
Richard Boys (Newcastle University) Bayesian inference for stochastic epidemic models with time-inhomogeneous removal rates
Brad Carlin (University of Minnesota) Spatial Point Process Models For Multivariate Data
Arnaud Doucet (University of British Columbia) The Expected Auxiliary Variable Method for Monte Carlo Simulation
Bhramar Mukherjee (University of Michigan) Using the Dirichlet Process Prior to Model Epidemiologic Data
Tobias Rydén (Lund University) Pros and cons of Bayesian estimation of hidden Markov models

Invited contributed

Nicolas Chopin (Ecole Nationale de la Statistique et de l'Administration Economique, Paris) Likelihood inference for continuous-time hidden Markov models
Pierpaolo De Blasi (University of Turin) Bayesian survival analysis in proportional hazard models with logistic relative risk
Paul Fearnhead (Lancaster University) Bayesian analysis of the structure of GC content in the Human Genome
Andrew Golightly (Newcastle University) Bayesian inference for nonlinear multivariate diffusion processes
John Lau (University of Bristol) A class of generalized hyperbolic continuous time integrated stochastic volatility likelihood models
Miguel A. Martínez-Beneito (Epidemiology Service, Generalitat Valenciana and Universitat de València) Linking spatio-temporal disease mapping information
Raquel Montes (Universidad Rey Juan Carlos) Bayesian inference for Detecting Brain Activity in Fmri
Gonzalo García-Donato Layrón (Universidad de Castilla-La Mancha) Validation of computer models with multivariate functional outputs
Raquel Prado (University of California at Santa Cruz) Sequential estimation of features associated with states of mental alertness in EEG signals
Fabio Rigat (University of Warwick) Parallel hierarchical sampling
Marc Suchard (University of California at Los Angeles) Phylogenetic repeated measures models via a Brownian diffusion process
Zhen Wang (Ohio State university) Bayesian inference for a distribution-valued stochastic process