Bayesian nets and causality : philosophical and computational foundations / Jon Williamson.

Format:

Book

Author/Creator:

Williamson, Jon.

Language:

English

Subjects (All):

Bayesian statistical decision theory.

Causation.

Physical Description:

1 online resource (250 p.)

Place of Publication:

Oxford : Oxford University Press, 2005.

Language Note:

English

Summary:

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.

Contents:

Contents; 1 Introduction; 1.1 Philosophical Claims; 1.2 Computational Claims; 2 Probability; 2.1 Variables; 2.2 Probability Functions; 2.3 Interpretations and Distinctions; 2.4 Frequency; 2.5 Propensity; 2.6 Chance; 2.7 Bayesianism; 2.8 Chance as Ultimate Belief; 2.9 Applying Probability; 3 Bayesian Nets; 3.1 Bayesian Networks; 3.2 Independence and D-Separation; 3.3 Representing Probability Functions; 3.4 Inference in Bayesian Nets; 3.5 Constructing Bayesian Nets; 3.6 The Adding-Arrows Algorithm; 3.7 Adding Arrows: an Example; 3.8 The Approximation Subspace; 3.9 Greed of Adding Arrows

3.10 Complexity of Adding Arrows3.11 The Case for Adding Arrows; 4 Causal Nets: Foundational Problems; 4.1 Causally Interpreted Bayesian Nets; 4.2 Physical Causality, Physical Probability; 4.3 Mental Causality, Physical Probability; 4.4 Physical Causality, Mental Probability; 4.5 Mental Causality, Mental Probability; 5 Objective Bayesianism; 5.1 Objective versus Subjective; 5.2 The Origins of Objective Bayesianism; 5.3 Empirical Constraints: The Calibration Principle; 5.4 Logical Constraints: The Maximum Entropy Principle; 5.5 Maximising Entropy Efficiently

5.6 From Constraints to Markov Network5.7 From Markov to Bayesian Network; 5.8 Causal Constraints; 6 Two-Stage Bayesian Nets; 6.1 Causal Nets Maximise Entropy; 6.2 Refining Bayesian Nets; 6.3 A Two-Stage Methodology; 7 Causality; 7.1 Metaphysics of Causality; 7.2 Mechanisms; 7.3 Probabilistic Causality; 7.4 Counterfactuals; 7.5 Agency; 8 Discovering Causal Relationships; 8.1 Epistemology of Causality; 8.2 Hypothetico-Deductive Discovery; 8.3 Inductive Learning; 8.4 Constraint-Based Induction; 8.5 Bayesian Induction; 8.6 Information-Theoretic Induction; 8.7 Shafer's Causal Conjecturing

8.8 The Devil and the Deep Blue Sea9 Epistemic Causality; 9.1 Mental yet Objective; 9.2 Kant; 9.3 Ramsey; 9.4 The Convenience of Causality; 9.5 Causal Beliefs; 9.6 Special Cases; 9.7 Uniqueness and Objectivity; 9.8 Causal Knowledge; 9.9 Discovering Causal Relationships: A Synthesis; 9.10 The Analogy with Objective Bayesianism; 10 Recursive Causality; 10.1 Overview; 10.2 Causal Relations as Causes; 10.3 Extension to Recursive Causality; 10.4 Consistency; 10.5 Joint Distributions; 10.6 Related Proposals; 10.7 Structural Equation Models; 10.8 Argumentation Networks; 11 Logic; 11.1 Overview

11.2 Propositional Logic11.3 Bayesian Nets for Logical Reasoning; 11.4 Influence Relations; 11.5 Recursive Logical Nets; 11.6 The Effectiveness of Logical Nets; 11.7 Logic Programming and Logical Nets; 11.8 Logical Constraints and Logical Beliefs; 11.9 Probability Logic; 11.10 Partial Entailment; 11.11Semantics for Probability Logic; 11.12 Deciding Probabilistic Entailment; 12 Language Change; 12.1 Two Problems of Belief Change; 12.2 Language Contains Implicit Knowledge; 12.3 Goodman's New Problem of Induction; 12.4 The Principle of Indifference; 12.5 Indirect Evidence

12.6 Types of Language Change

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

0-19-152393-3

1-4294-8709-7

9786610845217

1-280-84521-X

OCLC:

437925017