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A Bayesian method for the induction of probabilistic networks from data
 MACHINE LEARNING
, 1992
"... This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction of probabili ..."
Abstract

Cited by 1316 (31 self)
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This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computerassisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.
Using Path Diagrams as a Structural Equation Modelling Tool
, 1997
"... this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include: ..."
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Cited by 34 (7 self)
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this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include:
A method for learning belief networks that contain hidden variables
 in Proceedings of the Workshop on Knowledge Discovery in Databases
, 1994
"... This paper presents a Bayesian method for computing the probability of a Bayesian beliefnetwork structure from a database. In particular, the paper focuses on computing the probability of a beliefnetwork structure that contains e. hidden (latent) variable. A hidden variable represents a postulated ..."
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Cited by 10 (4 self)
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This paper presents a Bayesian method for computing the probability of a Bayesian beliefnetwork structure from a database. In particular, the paper focuses on computing the probability of a beliefnetwork structure that contains e. hidden (latent) variable. A hidden variable represents a postulated entity about which we have no data. For example, we may wish to postulate the existence of a hidden
1Using Path Diagrams as a Structural Equation Modelling Tool
"... Linear structural equation models (SEMs) are widely used in sociology, econometrics, biology, and other sciences. A SEM (without free parameters) has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the “error ” ..."
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Linear structural equation models (SEMs) are widely used in sociology, econometrics, biology, and other sciences. A SEM (without free parameters) has two parts: a probability distribution (in the Normal case specified by a set of linear structural equations and a covariance matrix among the “error ” or “disturbance ” terms), and an associated path
ei'rn«re, Conditional Independence in Directed Cyclic Graphical Models for Feedback
, 1994
"... Conditional independence in directed cyclic graphical models for feedback ..."
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Conditional independence in directed cyclic graphical models for feedback