Highly Structured Stochastic Systems: (27 Oxford Statistical Science Series (0-19-961199-8))

Highly Structured Stochastic Systems (HSSS) is a modern strategy for building statistical models for challenging real-world problems, for computing with them, and for interpreting the resulting inferences. Complexity is handled by working up from simple local assumptions in a coherent way, and that is the key to modelling, computation, inference and interpretation; the unifying framework is that of Bayesian hierarchical models. The aim of this book is to make recent developments in HSSS accessible to a general statistical audience. Graphical modelling and Markov chain Monte Carlo (MCMC) methodology are central to the field, and in this text they are covered in depth. The chapters on graphical modelling focus on causality and its interplay with time, the role of latent variables, and on some innovative applications. Those on Monte Carlo algorithms include discussion of the impact of recent theoretical work on the evaluation of performance in MCMC, extensions to variable dimension problems, and methods for dynamic problems based on particle filters. Coverage of these underlying methodologies is balanced by substantive areas of application - in the areas of spatial statistics (with epidemiological, ecological and image analysis applications) and biology (including infectious diseases, gene mapping and evolutionary genetics). The book concludes with two topics (model criticism and Bayesian nonparametrics) that seek to challenge the parametric assumptions that otherwise underlie most HSSS models. Altogether there are 15 topics in the book, and for each there is a substantial article by a leading author in the field, and two invited commentaries that complement, extend or discuss the main article, and should be read in parallel. All authors are distinguished researchers in the field, and were active participants in an international research programme on HSSS. This is the 27th volume in the Oxford Statistical Science Series, which includes texts and monographs covering many topics of current research interest in pure and applied statistics. These texts focus on topics that have been at the forefront of research interest for several years. Other books in the series include: J.Durbin and S.J.Koopman: Time series analysis by State Space Models; Peter J. Diggle, Patrick Heagerty, Kung-Yee Liang, Scott L. Zeger: Analysis of Longitudinal Data 2/e; J.K. Lindsey: Nonlinear Models in Medical Statistics; Peter J. Green, Nils L. Hjort & Sylvia Richardson: Highly Structured Stochastic Systems; Margaret S. Pepe: Statistical Evaluation of Medical Tests.

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