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The book will certainly be appreciated by researchers and graduate students in computer science, mathematics and philosophy and, in particular, by all interested in the complicated relations between subjective and objective interpretations of probabilistic phenomena. (EMS Newsletter)
Bayesian Nets and Causality is a very well-written and well-organized book ... No doubt it will be recognized as a very important contribution to the philosophy of probability and causality by a young distinguished philosopher. (Sungho Choi, Mind)
Reseña del editor
Bayesian nets are widely 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. But many philosophers have criticised 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This book argues that the Causal Markov Condition holds as a default rule: it often holds but may need to be repealed in the face of counterexamples. 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 an exposition of the computational techniques that they motivate.
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- VerlagOUP Oxford
- Erscheinungsdatum2004
- ISBN 10 019853079X
- ISBN 13 9780198530794
- EinbandTapa dura
- SpracheEnglisch
- Anzahl der Seiten252
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Bayesian Nets and Causality - Philosophical and Computational Foundations
Verlag:
Oxford University Press, Oxford, 2005
ISBN 10: 019853079X
ISBN 13: 9780198530794
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BAYESIAN NETS AND CAUSALITY: PHILOSOPHICAL AND COMPUTATIONAL FOUNDATIONS
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ISBN 10: 019853079X
ISBN 13: 9780198530794
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ISBN 10: 019853079X
ISBN 13: 9780198530794
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Hardcover. Zustand: new. Hardcover. Bayesian nets are widely 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. But many philosophers have criticised 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 artificialintelligence?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 counterexamples. Thus Bayesian nets are the right tool to use bydefault 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 alsoobjective - 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 computerscience, mathematics and philosophy, provides a general introduction to these philosophical views as well as an exposition of the computational techniques that they motivate. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Bestandsnummer des Verkäufers 9780198530794
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