Highly Structured Stochastic Systems

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