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| Introduction | |
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| Philosophical Claims | |
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| Computational Claims | |
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| Probability | |
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| Variables | |
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| Probability Functions | |
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| Interpretations and Distinctions | |
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| Frequency | |
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| Propensity | |
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| Chance | |
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| Bayesianism | |
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| Chance as Ultimate Belief | |
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| Applying Probability | |
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| Bayesian Nets | |
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| Bayesian Networks | |
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| Independence and D-Separation | |
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| Representing Probability Functions | |
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| Inference in Bayesian Nets | |
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| Constructing Bayesian Nets | |
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| The Adding-Arrows Algorithm | |
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| Adding Arrows: an Example | |
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| The Approximation Subspace | |
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| Greed of Adding Arrows | |
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| Complexity of Adding Arrows | |
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| The Case for Adding Arrows | |
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| Causal Nets: Foundational Problems | |
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| Causally Interpreted Bayesian Nets | |
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| Physical Causality, Physical Probability | |
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| Mental Causality, Physical Probability | |
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| Physical Causality, Mental Probability | |
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| Mental Causality, Mental Probability | |
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| Objective Bayesianism | |
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| Objective versus Subjective | |
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| The Origins of Objective Bayesianism | |
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| Empirical Constraints: The Calibration principle | |
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| Logical Constraints: The Maximum Entropy Principle | |
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| Maximising Entropy Efficiently | |
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| From Constraints to Markov Network | |
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| From Markov to Bayesian Network | |
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| Causal Constraints | |
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| Two-Stage Bayesian Nets | |
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| Causal Nets Maximise Entropy | |
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| Refining Bayesian Nets | |
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| A Two-Stage Methodology | |
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| Causality | |
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| Metaphysics of Causality | |
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| Mechanisms | |
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| Probabilistic Causality | |
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| Counterfactuals | |
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| Agency | |
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| Discovering Causal Relationships | |
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| Epistemology of Causality | |
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| Hypothetico-Deductive Discovery | |
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| Inductive Learning | |
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| Constraint-Based Induction | |
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| Bayesian Induction | |
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| Information-Theoretic Induction | |
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| Shafer's Causal Conjecturing | |
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| The Devil and the Deep Blue Sea | |
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| Epistemic Causality | |
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| Mental yet Objective | |
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| Kant | |
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| Ramsey | |
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| The Convenience of Causality | |
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| Causal Beliefs | |
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| Special Cases | |
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| Uniqueness and Objectivity | |
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| Causal Knowledge | |
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| Discovering Causal Relationships: A Synthesis | |
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| The Analogy with Objective Bayesianism | |
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| Recursive Causality | |
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| Overview | |
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| Causal Relations as Causes | |
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| Extension to Recursive Causality | |
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| Consistency | |
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| Joint Distributions | |
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| Related Proposals | |
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| Structural Equation Models | |
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| Argumentation Networks | |
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| Logic | |
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| Overview | |
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| Propositional Logic | |
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| Bayesian Nets for Logical Reasoning | |
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| Influence Relations | |
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| Recursive Logical Nets | |
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| The Effectiveness of Logical Nets | |
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| Logic Programming and Logical Nets | |
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| Logical Constraints and Logical Beliefs | |
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| Probability Logic | |
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| Partial Entailment | |
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| Semantics for Probability Logic | |
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| Deciding Probabilistic Entailment | |
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| Language Change | |
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| Two Problems of Belief Change | |
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| Language Contains Implicit Knowledge | |
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| Goodman's New Problem of Induction | |
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| The Principle of Indifference | |
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| Indirect Evidence | |
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| Types of Language Change | |
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| Conservativity | |
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| Prospects for a Solution | |
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| Language Change Update Strategies | |
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| The Maximin Update Strategy | |
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| Cross Entropy Updating of Bayesian Nets | |
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| Compatibility and Indirect Evidence | |
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| The Maxent Update Strategy | |
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| References | |
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| Index | |