Highly Structured Stochastic Systems

Hardcover | November 7, 2003

byPeter J Green, Nils Lid Hjort, Sylvia Richardson

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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, andthat 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 onMonte 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 substantiveareas 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 seekto 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 wereactive 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 inthe 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 and Sylvia Richardson:Highly Structured Stochastic Systems; Margaret S. Pepe: Statistical Evaluation of Medical Tests.

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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, andthat is the key to modelling, computation, ...

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

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Format:HardcoverDimensions:536 pages, 9.21 × 6.14 × 1.26 inPublished:November 7, 2003Publisher:Oxford University PressLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0198510551

ISBN - 13:9780198510550

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Table of Contents

Peter Green, Nils Hjort, Sylvia Richardson: Introduction1. Steffen Lauritzen: Some modern applications of graphical modelsNanny Wermuth: Analysing social science data with graphical Markov modelsJulia Mortera: Analysis of DNA mixtures using Bayesian networks2. Philip Dawid: Causal inference using influence diagrams: the problem of partial complianceElja Arjas: Commentary: causality and statisticsJames Robins: Semantics of causal DAG models and the identification of direct and indirect effects3. Thomas S. Richardson and Peter Sprites: Causal inference via ancestral graph modelsMilan Studeny: Other approaches to description of conditional independence structuresJan Koster: On ancestral graph Markov models4. Rainer Dahlhaus and Michael Eichler: Causality and graphical models in times series analysisVanessa Didelez: Graphical models for stochastic processesHans Kunsch: Discussion of "Causality and graphical models in times series analysis"5. Gareth Roberts: Linking theory and practice of MCMCChristian Robert: Advances in MCMC: a discussionArnoldo Frigessi: On some current research in MCMC6. Peter Green: Trans-dimensional Markov chain Monte CarloSimon Godsill: Proposal densities and product space methodsJuha Heikkinen: Trans-dimensional Bayesian nonparametrics with spatial point processes7. Carlo Berzuini and Walter Gilks: Particle filtering methods for dynamic and static Bayesian problemsGeir Storvik: Some further topics on Monte Carlo methods for dynamic Bayesian problemsPeter Clifford: General principles in sequential Monte Carlo methods8. Sylvia Richardson: Spatial models in epidemiological applicationsLeonhard Knorr-Held: Some remarks on Gaussian Markov random field modelsJesper Moller: A compariosn of spatial point process models in epidemiological applications9. Antti Penttinen, Fabio Divino and Anne Riiali: Spatial hierarchical Bayesian modeld in ecological applicationsJulian Besag: Likelihood analysis of binary data in space and timeAlexandro Mello Schmidt: Some further aspects of spatio-temporal modelling10. Merrilee Hurn; Oddvar Husby and Havard Rue: Advances in Bayesian image analysisM van Lieshout: Probabilistic image modellingAlain Trubuil: Prospects in Bayesian image analysis11. Niels Becker and Sergey Utev: Preventing epidemics in heterogeneous environmentsPhilip O'Neill: MCMC methods for stochastic epidemic modelsKari Auranen: Towards Bayesian inference in epidemic models12. Simon Heath: Genetic linkage analysis using Markov chain Monte Carlo techniquesNuala Sheehan and Daniel Sorensen: Graphical models for mapping continuous traitsDavid Stephens: Statistical approaches to Genetic Mapping13. R C Griffiths and Simon Tavare: The genealogy of neutral mutationGunter Weiss: Linked versus unlinked DNA data - a comparison based on ancestral inferenceCarsten Wiuf: The age of a rare mutation14. Anthony O'Hagan: HSSS model criticismM J Bayarri: What 'base' distribution for model criticism?Alan Gelfand: Some comments on model criticism15. Nils Hjort: Topics in nonparametric Bayesian statisticsAad van der Vaart: Asymptotics of Nonparametirc PosteriorsSonia Petrone: A predictive point of view on Bayesian nonparametrics