Philip Dawid

Carlo Berzuini

Luisa Bernardinell

A state of the art volume on statistical causality Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality. It covers the various formalisms in current use, methods for applying them to specific problems, and the special requirements of a range of examples from medicine, biology and economics to political science. This book: Provides a clear account and comparison of formal languages, concepts and models for statistical causality. Addresses examples from medicine, biology, economics and political science to aid the reader's understanding. Is authored by leading experts in their field. Is written in an accessible style. Postgraduates, professional statisticians and researchers in academia and industry will benefit from this book.

Statistical Perspectives and Applications

Causality

Stephen S. Senn

Mike Christie

Andrew Cliffe

Several points of disagreement exist between different modelling traditions as to whether complex models are always better than simpler models, as to how to combine results from different models and how to propagate model uncertainty into forecasts. This book represents the result of collaboration between scientists from many disciplines to show how these conflicts can be resolved. Key Features: Introduces important concepts in modelling, outlining different traditions in the use of simple and complex modelling in statistics. Provides numerous case studies on complex modelling, such as climate change, flood risk and new drug development. Concentrates on varying models, including flood risk analysis models, the petrol industry forecasts and summarizes the evolution of water distribution systems. Written by experienced statisticians and engineers in order to facilitate communication between modellers in different disciplines. Provides a glossary giving terms commonly used in different modelling traditions. This book provides a much-needed reference guide to approaching statistical modelling. Scientists involved with modelling complex systems in areas such as climate change, flood prediction and prevention, financial market modelling and systems engineering will benefit from this book. It will also be a useful source of modelling case histories.

Simplicity, Complexity and Modelling

Steffen L. Lauritzen

Robert G. Cowell

David J. Spiegelhalter

The work reviewed in this book represents the synthesis of two important developments in modelling of complex stochastic phenomena. The book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms.

Exact Computational Methods for Bayesian Networks

Probabilistic Networks and Expert Systems

Philip Dawid

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