Logic

Bayesian Nets and Causality: Philosophical and Computational Foundations

Name: Bayesian Nets and Causality: Philosophical and Computational Foundations
Brand: OUP Oxford
SKU: 9780198530794
Price: 17558.00 INR
Availability: InStock
ISBN: 9780198530794

BAYESIAN NETS & CAUSALITY

Contributor(s): Williamson, Jon Author Format: Hardback

Hardback · ISBN 9780198530794

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Key Specifications

ISBN	9780198530794
Subtitle	BAYESIAN NETS & CAUSALITY
Publisher	OUP Oxford
Publication date	December 2004
Language	English
MRP	£125.00 GBP
Page count	252
Item weight	117 g

Bayesian nets are widely used in artificial intelligence as a calculus for casual reasoning, enabling machines to make predictions, perform diagnoses, take decisions and even to discover casual relationships. But many philosophers have criticized and ultimately rejected the central assumption on which such work is based-the causal Markov Condition. So should Bayesian nets be abandoned? What explains their success in artificial intelligence? This book argues that the Causal Markov Condition holds as a default rule: it often holds but may need to be repealed in the face of counter examples. Thus, Bayesian nets are the right tool to use by default but naively applying them can lead to problems. The book develops a systematic account of causal reasoning and shows how Bayesian nets can be coherently employed to automate the reasoning processes of an artificial agent. The resulting framework for causal reasoning involves not only new algorithms, but also new conceptual foundations. Probability and causality are treated as mental notions - part of an agent's belief state. Yet probability and causality are also objective - different agents with the same background knowledge ought to adopt the same or similar probabilistic and causal beliefs. This book, aimed at researchers and graduate students in computer science, mathematics and philosophy, provides a general introduction to these philosophical views as well as exposition of the computational techniques that they motivate.

Bayesian Nets and Causality: Philosophical and Computational Foundations

Key Specifications

Williamson, Jon Author

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