Highly Structured Stochastic Systems

TRAMA

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.

SOMMARIO

1 - Some modern applications of graphical models2 - Causal inference using influence diagrams: the problem of partial compliance3 - Causal inference via ancestral graph models4 - Causality and graphical models in times series analysis5 - Linking theory and practice of MCMC6 - Trans-dimensional Markov chain Monte Carlo7 - Particle filtering methods for dynamic and static Bayesian problems8 - Spatial models in epidemiological applications9 - Spatial hierarchical Bayesian modeld in ecological applications10 - Advances in Bayesian image analysis11 - Preventing epidemics in heterogeneous environments12 - Genetic linkage analysis using Markov chain Monte Carlo techniques13 - The genealogy of neutral mutation14 - HSSS model criticism15 - Topics in nonparametric Bayesian statistics

AUTORE

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

ALTRE INFORMAZIONI

• Condizione: Nuovo
• ISBN: 9780198510550
• Collana: Oxford Statistical Science Series (0-19-961199-8)
• Dimensioni: 240 x 32.0 x 159 mm Ø 874 gr
• Formato: Copertina rigida
• Illustration Notes: numerous figures
• Pagine Arabe: 532