Highly Structured Stochastic Systems

Introduction 1. Some modern applications of graphical models Analysing social science data with graphical Markov models Analysis of DNA mixtures using Bayesian networks 2. Causal inference using influence diagrams: the problem of partial compliance Commentary: causality and statistics Semantics of causal DAG models and the identification of direct and indirect effects 3. Causal inference via ancestral graph models Other approaches to description of conditional independence structures On ancestral graph Markov models 4. and graphical models in times series Graphical models for stochastic processes Discussion of Causality and graphical models in times series analysis 5. Linking theory and practice of MCMC Advances in MCMC: a discussion On some current research in MCMC 6. Trans-dimensional Markov chain Monte Carlo Proposal densities and product space methods Trans-dimensional Bayesian nonparametrics with spatial point processes 7. Particle filtering methods for dynamic and static Bayesian problems Some further topics on Monte Carlo methods for dynamic Bayesian problems General principles in sequential Monte Carlo methods 8. Spatial models in epidemiological applications Some remarks on Gaussian Markov random field models A compariosn of spatial point process models in epidemiological applications 9. Spatial hierarchical Bayesian modeld in ecological applications Likelihood of binary data in space and time Some further aspects of spatio-temporal modelling 10. Advances in Bayesian image Probabilistic image modelling Prospects in Bayesian image 11. Preventing epidemics in heterogeneous environments MCMC methods for stochastic epidemic models Towards Bayesian inference in epidemic models 12. Genetic linkage using Markov chain Monte Carlo techniques Graphical models for mapping continuous traits Statistical approaches to Genetic Mapping 13. The genealogy of neutral mutation Linked versus unlinked DNA data - a comparison based on ancestral inference The age of a rare mutation 14. HSSS model criticism What 'base' distribution for model criticism? Some comments on model criticism 15. Topics in nonparametric Bayesian statistics Asymptotics of Nonparametirc Posteriors A predictive point of view on Bayesian nonparametrics