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The art of causal conjecture

Author: Glenn Shafer
Publisher: Cambridge, Mass. MIT Press 1996
Series: Artificial intelligence.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:

In The Art of Causal Conjecture, Glenn Shafer lays out a new mathematical and philosophical foundation for probability and uses it to explain concepts of causality used in statistics, artificial  Read more...

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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Glenn Shafer
ISBN: 026219368X 9780262193689
OCLC Number: 832507731
Notes: Literaturverz. S. [491] - 500
Description: XX, 511 Seiten Illustrationen, Diagramme
Contents: Part 1 Introduction: probability trees; many observers, many stances, many natures; causal relations as relations in nature's tree; evidence; measuring the average effect of a cause; causal diagrams; Humean events; three levels of causal language; an outline of the book. Part 2 Event trees: situations and events; the ordering of situations and Moivrean events; cuts; Humean events; Moivrean variables; Humean variables; event trees for stochastic processes; timing in event trees; intersecting event trees; notes on the literature. Part 3 Probability trees: some types of probability trees; axioms for the probabilities of Moivrean events; zero probabilities; a sample-space analysis of the event-tree axioms; probabilities and expected values for variables; Martingales; the expectation of a variable in a cut; conditional expected value and conditional expectation. Part 4 The meaning of probability: the interpretation of expected value; the interpretation of expectation; the long run; changes in belief; the empirical validation of probability; the diversity of uses of probability; notes on the literature. Part 5 Independent events: independence; weak independence; the principle of the common cause; conditional independence; notes on the literature. Part 6 Events tracking events: tracking; tracking and conditional independence; stochastic subsequence; singular diagrams for stochastic subsequence; conjunctive and interactive forks. Part 7 Events as signs of events: sign; weak sign; the ethics of causal talk; screening off. Part 8 Independent variables: unconditional independence; conditional independence; independence for partitions; independence for families of variables; individual properties of the independence relations. Part 9 Variables tracking variables: tracking and conditional independence - a summary; strong tracking; strong tracking and conditional independence; stochastic subsequence; functional dependence; tracking in mean; linear tracking; tracking by partitions; tracking by families of variables. Part 10 Variables as signs of variables: sign; linear sign; scored sign; families of variables. Part 11 An abstract theory of event trees: event trees as sets of sets; event trees as partially ordered sets; regular event trees; the resolution of Moivrean variables; Humean events and variables. Part 12 Martingale trees: examples of decision trees; the meaning of probability in a decision tree; Martingales; the structures of Martingale trees; probability and causality; lower and upper probability; the law of large numbers; notes on the literature. Part 13 Refining; examples of refinement; a constructive definition of finite refinement; axioms for refinement; lifting Moivrean events and variables; refining Martingale trees; grounding. Part 14 Principles of causal conjecture: the diversity of causal explanation; the mean effect of the happening of a Moivrean event. (Part contents).
Series Title: Artificial intelligence.
Responsibility: Glenn Shafer
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"Causality plays an important role in many fields, from engineering to medicine to artificial intelligence. Glenn Shafer has written an important, scholarly study of causality. He starts with a novel Read more...

 
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