Table of Contents

Peter Green, Nils Hjort, Sylvia Richardson: Introduction
1: Steffen Lauritzen: Some modern applications of graphical models
Nanny Wermuth: Analysing social science data with graphical Markov models
Julia Mortera: Analysis of DNA mixtures using Bayesian networks
2: Philip Dawid: Causal inference using influence diagrams: the problem of partial compliance
Elja Arjas: Commentary: causality and statistics
James Robins: Semantics of causal DAG models and the identification of direct and indirect effects
3: Thomas S. Richardson and Peter Sprites: Causal inference via ancestral graph models
Milan Studeny: Other approaches to description of conditional independence structures
Jan Koster: On ancestral graph Markov models
4: Rainer Dahlhaus and Michael Eichler: Causality and graphical models in times series analysis
Vanessa Didelez: Graphical models for stochastic processes
Hans Kunsch: Discussion of "Causality and graphical models in times series analysis"
5: Gareth Roberts: Linking theory and practice of MCMC
Christian Robert: Advances in MCMC: a discussion
Arnoldo Frigessi: On some current research in MCMC
6: Peter Green: Trans-dimensional Markov chain Monte Carlo
Simon Godsill: Proposal densities and product space methods
Juha Heikkinen: Trans-dimensional Bayesian nonparametrics with spatial point processes
7: Carlo Berzuini and Walter Gilks: Particle filtering methods for dynamic and static Bayesian problems
Geir Storvik: Some further topics on Monte Carlo methods for dynamic Bayesian problems
Peter Clifford: General principles in sequential Monte Carlo methods
8: Sylvia Richardson: Spatial models in epidemiological applications
Leonhard Knorr-Held: Some remarks on Gaussian Markov random field models
Jesper Moller: A compariosn of spatial point process models in epidemiological applications
9: Antti Penttinen, Fabio Divino and Anne Riiali: Spatial hierarchical Bayesian modeld in ecological applications
Julian Besag: Likelihood analysis of binary data in space and time
Alexandro Mello Schmidt: Some further aspects of spatio-temporal modelling
10: Merrilee Hurn; Oddvar Husby and Havard Rue: Advances in Bayesian image analysis
M van Lieshout: Probabilistic image modelling
Alain Trubuil: Prospects in Bayesian image analysis
11: Niels Becker and Sergey Utev: Preventing epidemics in heterogeneous environments
Philip O'Neill: MCMC methods for stochastic epidemic models
Kari Auranen: Towards Bayesian inference in epidemic models
12: Simon Heath: Genetic linkage analysis using Markov chain Monte Carlo techniques
Nuala Sheehan and Daniel Sorensen: Graphical models for mapping continuous traits
David Stephens: Statistical approaches to Genetic Mapping
13: R C Griffiths and Simon Tavare: The genealogy of neutral mutation
Gunter Weiss: Linked versus unlinked DNA data - a comparison based on ancestral inference
Carsten Wiuf: The age of a rare mutation
14: Anthony O'Hagan: HSSS model criticism
M J Bayarri: What 'base' distribution for model criticism?
Alan Gelfand: Some comments on model criticism
15: Nils Hjort: Topics in nonparametric Bayesian statistics
Aad van der Vaart: Asymptotics of Nonparametirc Posteriors
Sonia Petrone: A predictive point of view on Bayesian nonparametrics

About the Author

Peter J. Green
Professor of Statistics, University of Bristol Nils Lid Hjort
Professor of mathematical statistics, University of Oslo Sylvia Richardson
Professor of Biostatistics, Imperial College