Philip Dawid – författare

Evidence - its nature and interpretation - is the key to many topical debates and concerns such as global warming, evolution, the search for weapons of mass destruction, DNA profiling, evidence-based medicine. In 2004 University College London launched a cross-disciplinary research programme "Evidence, Inference and Enquiry" to explore the question: "Can there be an integrated multidisciplinary science of evidence?" While this question was hotly contested and no clear final consensus emerged, much was learned on the journey. This book, based on the closing conference of the programme held at the British Academy in December 2007, illustrates the complexity of the subject, with 17 chapters written from a diversity of perspectives including Archaeology, Computer Science, Economics, Education, Health, History, Law, Psychology, Philosophy and Statistics. General issues covered include principles and systems for handling complex evidence, evidence for policy-making, and human evidence-processing, as well as the very possibility of systematising the study of evidence.

WINNER OF THE 2001 DEGROOT PRIZE!Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors over a number of years, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases in which exact answers are obtainable. It covers both the updating of probabilistic uncertainty in the light of new evidence, and statistical inference, about unknown probabilities or unknown model structure, in the light of new data. The careful attention to detail will make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems.This book was awarded the first DeGroot Prize by the International Society for Bayesian Analysis for a book making an important, timely, thorough, and notably original contribution to the statistics literature.

Winner of the 2002 DeGroot Prize. Awarded by the International Society for Bayesian Analysis to a book judged to represent an important, timely, thorough, and notably original contribution to the statistics literature.Probabilistic expert systems are graphical networks that support the modelling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors over a number of years, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms, emphasizing those cases in which exact answers are obtainable. It covers both the updating of probabilistic uncertainty in the light of new evidence, and statistical inference, about unknown probabilities or unknown model structure, in the light of new data.The book will be of interest to researchers and graduate students in artificial intelligence who desire an understanding of the mathematical and statistical basis of probabilistic expert systems, and to students and research workers in statistics wanting an introduction to this fascinating and rapidly developing field.The careful attention to detail will also make this work an important reference source for all those involved in the theory and applications of probabilistic expert systems.Robert G. Cowell is a Lecturer in the Faculty of Actuarial Science and Statistics of the Sir John Cass Business School, City of London. He has been working in the field of probabilistic expert systems for over a decade, and has published a number of research and tutorial articles in the area.A. Philip Dawid is Pearson Professor of Statistics at University College London. He has served as Editor of the Journal of the Royal Statistical Society (Series B) and of Biometrika, and as President of the International Society for Bayesian Analysis. He holds the Royal Statistical Society Guy Medal in Bronze and in Silver, and the G. W. Snedecor Award for the Best Publication in Biometry.Steffen L. Lauritzen is Professor of Mathematics and Statistics at the University of Aalborg. He has served as Editor of the Scandinavian Journal of Statistics. He holds the Royal Statistical Guy Medal in Silver and is an Honorary Fellow of the same society. He has, jointly with David J.Spiegelhalter, received the American Statistical Association's award for an "Outstanding Statistical Application."David J. Spiegelhalter is a Senior Scientist at the MRC Biostatistics Unit in the Cambridge University Institute of Public Health. He has published extensively on Bayesian methodology and applications, and holds the Royal Statistical Society Guy Medal in Bronze and in Silver.

Beauty challenges conventional approaches to the subject through an interdisciplinary approach that forges connections between the arts, sciences and mathematics. Classical, conventional aspects of beauty are addressed in subtle, unexpected ways: symmetry in mathematics, attraction in the animal world and beauty in the cosmos. This collection arises from the Darwin College Lecture Series of 2011 and includes essays from eight distinguished scholars, all of whom are held in esteem not only for their research but also for their ability to communicate their subject to popular audiences. Each essay is entertaining, accessible and thought-provoking and is accompanied by images illustrating beauty in practice. Contributors include the artist José Hernández, Nobel Prize Laureate Frank Wilczek, Lord May of Oxford and Jeanne Altmann (Eugene Higgins Professor of Ecology and Evolutionary Biology at Princeton University).

A state of the art volume on statistical causalityCausality: 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.