Data mining tasks and methods: Probabilistic and casual networks: methodology for probabilistic networks

Authors:
Peter L. Spirtes
Affiliations:
Professor of Philosophy, Carnegie Mellon University, Pittsburgh, Pennsylvania
Venue:
Handbook of data mining and knowledge discovery
Year:
2002

Citing 14
Cited 0

Fusion, propagation, and structuring in belief networks

Artificial Intelligence
Evaluating influence diagrams

Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
The computational complexity of probabilistic inference using Bayesian belief networks (research note)

Artificial Intelligence
Applying Bayesian networks to information retrieval

Communications of the ACM
BIFROST—block recursive models induced from relevant knowledge, observations, and statistical techniques

Computational Statistics & Data Analysis - Special issue dedicated to Toma´sˇ Havra´nek
Probabilistic independence networks for hidden Markov probability models

Neural Computation
Introduction to Bayesian Networks

Introduction to Bayesian Networks
A Guide to the Literature on Learning Probabilistic Networks from Data

IEEE Transactions on Knowledge and Data Engineering
Mean field theory for sigmoid belief networks

Journal of Artificial Intelligence Research
The lumière project: Bayesian user modeling for inferring the goals and needs of software users

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Probabilistic evaluation of sequential plans from causal models with hidden variables

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Directed cyclic graphical representations of feedback models

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Computing upper and lower bounds on likelihoods in intractable networks

UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This article provides an overview of the uses, methods of construction, and interpretations of Bayesian networks. Bayesian networks have two distinct interpretations. Under the probabilistic interpretation, a Bayesian network consists of a directed acyclic graph over a set of random variables, and represents a set of probability distributions, all of which share certain conditional independence relations described by a Markov property. Interpreted in this way, a Bayesian network is a device that provides a means of eliciting probabilities from an expert, a compact representation of a probability distribution, and a means for quickly calculating arbitrary conditional probabilities. Under the causal interpretation, a Bayesian network is a directed acyclic graph where an edge represents direct causal relations between random variables. Under the causal intepretation, the Bayesian network can be used to calculate the effects of intervening on an existing causal system by manipulating the values of variables. Methods of construction are briefly described, and several examples are given.