Jon Williamson

Bayesian nets are used in artificial intelligence as a calculus for causal reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover causal relationships. This book brings together how to automate reasoning in artificial intelligence, and the nature of causality and probability in philosophy.

Bayesian Nets and Causality: Philosophical and Computational Foundations

In defence of objective Bayesianism

D. Corfield

Dr. Jon Williamson

This is an authoritative collection of papers addressing the key challenges that face the Bayesian interpretation of probability today. The volume includes important criticisms of Bayesian reasoning and gives an insight into some of the points of disagreement amongst advocates of the Bayesian approach. It will be of interest to graduate students, researchers, those involved with the applications of Bayesian reasoning, and philosophers.

Foundations of Bayesianism

Federica Russo

Causal inference is perhaps the most important form of reasoning in the sciences. A panoply of disciplines, ranging from epidemiology to biology, from econometrics to physics, make use of probability and statistics in order to infer causal relationships. However, the very foundations of causal inference are up in the air; it is by no means clear which methods of causal inference should be used, nor why they work when they do. This book brings philosophers and scientists together to tackle these important questions. The papers in this volume shed light on the relationship between causality and probability and the application of these concepts within the sciences. With its interdisciplinary perspective and its careful analysis, "Causality and Probability in the Sciences" heralds the transition of causal inference from an art to a science.

Causality and Probability in the Sciences

Phyllis McKay Illari

Why do ideas of how mechanisms relate to causality and probability differ so much across the sciences? Can progress in understanding the tools of causal inference in some sciences lead to progress in others? This book tackles these questions and others concerning the use of causality in the sciences.

Causality in the Sciences

An accessible guide for those facing the study of Logic For The first time, this book covers key thinkers, terms and texts.

Key Terms in Logic

Beth Shaw

Phyllis Illari

Veli-Pekka Parkkinen

Christian Wallmann

Michael Wilde

Brendan Clarke

Michael P Kelly

Charles Norell

This book is open access under a CC BY license. This book is the first to develop explicit methods for evaluating evidence of mechanisms in the field of medicine. It explains why it can be important to make this evidence explicit, and describes how to take such evidence into account in the evidence appraisal process. In addition, it develops procedures for seeking evidence of mechanisms, for evaluating evidence of mechanisms, and for combining this evaluation with evidence of association in order to yield an overall assessment of effectiveness. Evidence-based medicine seeks to achieve improved health outcomes by making evidence explicit and by developing explicit methods for evaluating it. To date, evidence-based medicine has largely focused on evidence of association produced by clinical studies. As such, it has tended to overlook evidence of pathophysiological mechanisms and evidence of the mechanisms of action of interventions. The book offers a useful guide for all those whose work involves evaluating evidence in the health sciences, including those who need to determine the effectiveness of health interventions and those who need to ascertain the effects of environmental exposures.

Jon Williamson

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