• Publications
  • Influence
Probabilistic reasoning in intelligent systems - networks of plausible inference
  • J. Pearl
  • Computer Science, Philosophy
  • Morgan Kaufmann series in representation and…
  • 1 August 1991
TLDR
Probabilistic Reasoning in Intelligent Systems is a complete andaccessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. Expand
  • 17,536
  • 1768
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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.Expand
  • 9,706
  • 1314
  • PDF
Probabilistic reasoning in intelligent systems
TLDR
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty. Expand
  • 4,298
  • 574
  • PDF
Temporal Constraint Networks
TLDR
This paper extends network-based methods of constraint satisfaction to include continuous variables, thus providing a framework for processing temporal constraints that permits the processing of metric information, namely, assessments of time differences between events. Expand
  • 2,033
  • 336
  • PDF
Direct and Indirect Effects
  • J. Pearl
  • Computer Science, Mathematics
  • UAI
  • 2 August 2001
TLDR
This paper presents a new definition of path-specific effects that is based on path switching, instead of vari­ able fixing, and that extends the interpretation and evaluation of direct and indirect effects to nonlinear models. Expand
  • 1,262
  • 192
  • PDF
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 paperExpand
  • 1,502
  • 162
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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. Expand
  • 1,071
  • 101
Fusion, Propagation, and Structuring in Belief Networks
  • J. Pearl
  • Computer Science
  • Artif. Intell.
  • 1 September 1986
TLDR
Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. Expand
  • 2,050
  • 100
  • PDF
Heuristics - intelligent search strategies for computer problem solving
  • J. Pearl
  • Computer Science
  • Addison-Wesley series in artificial intelligence
  • 1 April 1984
This book presents, characterizes and analyzes problem solving strategies that are guided by heuristic information
  • 1,768
  • 97
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 toExpand
  • 973
  • 88
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