Probabilistic reasoning in intelligent systems - networks of plausible inference
- J. Pearl
- Computer ScienceMorgan Kaufmann series in representation and…
- 1 August 1991
The author provides a coherent explication of probability as a language for reasoning with partial belief and offers a unifying perspective on other AI approaches to uncertainty, such as the Dempster-Shafer formalism, truth maintenance systems, and nonmonotonic logic.
Causality: Models, Reasoning and Inference
- J. Pearl
- Philosophy
- 2000
1. Introduction to probabilities, graphs, and causal models 2. A theory of inferred causation 3. Causal diagrams and the identification of causal effects 4. Actions, plans, and direct effects 5.…
Probabilistic reasoning in intelligent systems
- J. Pearl
- Computer Science
- 1988
Temporal Constraint Networks
- R. Dechter, Itay Meiri, J. Pearl
- Computer ScienceArtificial Intelligence
- 1 December 1989
Direct and Indirect Effects
- J. Pearl
- EconomicsConference on Uncertainty in Artificial…
- 2 August 2001
A new way of defining the effect transmitted through a restricted set of paths, without controlling variables on the remaining paths is presented, which permits the assessment of a more natural type of direct and indirect effects.
Causal diagrams for empirical research
- J. Pearl
- Philosophy
- 1 December 1995
SUMMARY The primary aim of this paper is to show how graphical models can be used as a mathematical language for integrating statistical and subject-matter information. In particular, the paper…
Causal inference in statistics: An overview
- J. Pearl
- Philosophy
- 15 July 2009
This review presents empiricalresearcherswith recent advances in causal inference, and stresses the paradigmatic shifts that must be un- dertaken in moving from traditionalstatistical analysis to…
Heuristics - intelligent search strategies for computer problem solving
- J. Pearl
- PsychologyAddison-Wesley series in artificial intelligence
- 1 April 1984
This book presents, characterizes and analyzes problem solving strategies that are guided by heuristic information and provides examples of how these strategies have changed over time.
Fusion, Propagation, and Structuring in Belief Networks
- J. Pearl
- Computer ScienceArtificial Intelligence
- 1 September 1986
It is shown that if the network is singly connected (e.g. tree-structured), then probabilities can be updated by local propagation in an isomorphic network of parallel and autonomous processors and that the impact of new information can be imparted to all propositions in time proportional to the longest path in the network.
Equivalence and Synthesis of Causal Models
- Thomas Verma, J. Pearl
- Computer ScienceConference on Uncertainty in Artificial…
- 27 July 1990
The canonical representation presented here yields an efficient algorithm for determining when two embedded causal models reflect the same dependency information, which leads to a model theoretic definition of causation in terms of statistical dependencies.
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