• Publications
  • Influence
Probabilistic reasoning in intelligent systems - networks of plausible inference
  • J. Pearl
  • Computer Science
    Morgan Kaufmann series in representation and…
  • 1 August 1991
TLDR
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
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
TLDR
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.
Temporal Constraint Networks
TLDR
It is shown that the STP, which subsumes the major part of Vilain and Kautz's point algebra, can be solved in polynomial time and the applicability of path consistency algorithms as preprocessing of temporal problems is studied, to demonstrate their termination and bound their complexities.
Direct and Indirect Effects
  • J. Pearl
  • Computer Science, Mathematics
    UAI
  • 2 August 2001
TLDR
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
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
Heuristics - intelligent search strategies for computer problem solving
  • J. Pearl
  • Computer Science
    Addison-Wesley series in artificial intelligence
  • 1 April 1984
TLDR
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.
Equivalence and synthesis of causal models
TLDR
A canonical representation for causal models is presented which yields an efficient graphical crite­ rion for deciding equivalence, and provides a theoret­ ical basis for extracting causal structures from em­ pirical data.
Causal inference in statistics: An overview
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
Fusion, Propagation, and Structuring in Belief Networks
  • J. Pearl
  • Computer Science
    Artif. Intell.
  • 1 September 1986
TLDR
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.
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