Evidential reasoning using stochastic simulation of causal models
type: Item
instance of: scholarly article
title: Evidential reasoning using stochastic simulation of causal models
zbMATH Open document ID: 0642.68177
DOI: 10.1016/0004-3702(87)90012-9
author: Judea Pearl
published in: Artificial Intelligence
publication date: 1987-01-01T00:00:00Z
review text: Stochastic simulation is a method of computing probabilities by recording the fraction of time that events occur in a random series of scenarios generated from some causal model. This paper presents an efficient, concurrent method of conducting the simulation which guarantees that all generated scenarios will be consistent with the observed data. It is shown that the simulation can be performed by purely local computations, involving products of parameters given with the initial specification of the model. Thus, the method proposed renders stochastic simulation a powerful technique of coherent inferencing, especially suited for tasks involving complex, nondecomposable models where ``ballpark'' estimates of probabilities will suffice.
Mathematics Subject Classification ID: 68T99
Mathematics Subject Classification ID: 90C40
zbMATH DE Number: 4047205
zbMATH Keywords: random series of scenarios
zbMATH Keywords: stochastic simulation
describes a project that uses: NESTOR
MaRDI profile type: Publication
full work available at URL: 0004-3702(87)90012-9
OpenAlex ID: W2093976733
cites work: Incidence calculus: A mechanism for probabilistic reasoning
cites work: The structure of the “THE”-multiprogramming system
cites work: Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
cites work: Q3795276
cites work: Fusion, propagation, and structuring in belief networks
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description: scientific article; zbMATH DE number 4047205