Results 1  10
of
20
A Bayesian method for the induction of probabilistic networks from data
 Machine Learning
, 1992
"... Abstract. 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 ..."
Abstract

Cited by 1095 (26 self)
 Add to MetaCart
Abstract. 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.
Operations for Learning with Graphical Models
 Journal of Artificial Intelligence Research
, 1994
"... This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models ..."
Abstract

Cited by 252 (12 self)
 Add to MetaCart
This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feedforward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...
A Guide to the Literature on Learning Probabilistic Networks From Data
, 1996
"... This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the ..."
Abstract

Cited by 172 (0 self)
 Add to MetaCart
This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples. Keywords Bayesian networks, graphical models, hidden variables, learning, learning structure, probabilistic networks, knowledge discovery. I. Introduction Probabilistic networks or probabilistic gra...
Learning Bayesian Networks from Data: An InformationTheory Based Approach
"... This paper provides algorithms that use an informationtheoretic analysis to learn Bayesian network structures from data. Based on our threephase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional indepe ..."
Abstract

Cited by 97 (5 self)
 Add to MetaCart
This paper provides algorithms that use an informationtheoretic analysis to learn Bayesian network structures from data. Based on our threephase learning framework, we develop efficient algorithms that can effectively learn Bayesian networks, requiring only polynomial numbers of conditional independence (CI) tests in typical cases. We provide precise conditions that specify when these algorithms are guaranteed to be correct as well as empirical evidence (from real world applications and simulation tests) that demonstrates that these systems work efficiently and reliably in practice.
A Theory of Learning Classification Rules
, 1992
"... The main contributions of this thesis are a Bayesian theory of learning classification rules, the unification and comparison of this theory with some previous theories of learning, and two extensive applications of the theory to the problems of learning class probability trees and bounding error whe ..."
Abstract

Cited by 80 (6 self)
 Add to MetaCart
The main contributions of this thesis are a Bayesian theory of learning classification rules, the unification and comparison of this theory with some previous theories of learning, and two extensive applications of the theory to the problems of learning class probability trees and bounding error when learning logical rules. The thesis is motivated by considering some current research issues in machine learning such as bias, overfitting and search, and considering the requirements placed on a learning system when it is used for knowledge acquisition. Basic Bayesian decision theory relevant to the problem of learning classification rules is reviewed, then a Bayesian framework for such learning is presented. The framework has three components: the hypothesis space, the learning protocol, and criteria for successful learning. Several learning protocols are analysed in detail: queries, logical, noisy, uncertain and positiveonly examples. The analysis is done by interpreting a protocol as a...
Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory
, 1997
"... This paper addresses the problem of learning Bayesian network structures from data by using an information theoretic dependency analysis approach. Based on our threephase construction mechanism, two efficient algorithms have been developed. One of our algorithms deals with a special case where the ..."
Abstract

Cited by 37 (0 self)
 Add to MetaCart
This paper addresses the problem of learning Bayesian network structures from data by using an information theoretic dependency analysis approach. Based on our threephase construction mechanism, two efficient algorithms have been developed. One of our algorithms deals with a special case where the node ordering is given, the algorithm only require ) ( 2 N O CI tests and is correct given that the underlying model is DAGFaithful [Spirtes et. al., 1996]. The other algorithm deals with the general case and requires ) ( 4 N O conditional independence (CI) tests. It is correct given that the underlying model is monotone DAGFaithful (see Section 4.4). A system based on these algorithms has been developed and distributed through the Internet. The empirical results show that our approach is efficient and reliable. 1 Introduction The Bayesian network is a powerful knowledge representation and reasoning tool under conditions of uncertainty. A Bayesian network is a directed acyclic graph ...
DIAVAL, a Bayesian expert system for echocardiography
 ARTIFICIAL INTELLIGENCE IN MEDICINE 10
, 1997
"... DIAVAL is an expert system for the diagnosis of heart diseases, based on several kinds of data, mainly from echocardiography. The first part of this paper is devoted to the causal probabilistic model which constitutes the knowledge base of the expert system in the form of a Bayesian network, emphasi ..."
Abstract

Cited by 12 (2 self)
 Add to MetaCart
DIAVAL is an expert system for the diagnosis of heart diseases, based on several kinds of data, mainly from echocardiography. The first part of this paper is devoted to the causal probabilistic model which constitutes the knowledge base of the expert system in the form of a Bayesian network, emphasizing the importance of the OR gate. The second part deals with the process of diagnosis, which consists of computing the a posteriori probabilities, selecting the most probable and most relevant diagnoses, and generating a written report. It also describes the results of the evaluation of the program.
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 ..."
Abstract

Cited by 10 (4 self)
 Add to MetaCart
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
An evaluation of the diagnostic accuracy
 of Pathfinder. Computers and Biomedical Research
, 1992
"... This work is an adaptation of Heckerman (1991). All figures and tables are printed with permission from MIT Press. We present an evaluation of the diagnostic accuracy of Pathfinder, an expert system that assists pathologists with the diagnosis of lymphnode diseases. We evaluate two versions of the ..."
Abstract

Cited by 8 (5 self)
 Add to MetaCart
This work is an adaptation of Heckerman (1991). All figures and tables are printed with permission from MIT Press. We present an evaluation of the diagnostic accuracy of Pathfinder, an expert system that assists pathologists with the diagnosis of lymphnode diseases. We evaluate two versions of the system using both informal and decisiontheoretic metrics of performance. In one version of Pathfinder, we assume incorrectly that all observations are conditionally independent. In the other version, we use a belief network to represent accurately the probabilistic dependencies among the observations. In both versions, we make the assumption—reasonable for this domain—that diseases are mutually exclusive and exhaustive. The results of the study show that (1) it is cost effective to represent probabilistic dependencies among observations in the lymphnode domain, and (2) the diagnostic accuracy of the more complex version of Pathfinder is at least as good as that of the Pathfinder expert. In addition, the study illustrates how informal and decisiontheoretic metrics for performance complement one another. 2 1
Decision analysis techniques for knowledge acquisition: Combining information and preference models using Aquinas
 Proceedings of the Second AAAI Knowledge Acquisition for KnowledgeBased Systems Workshop
, 1987
"... The field of decision analysis is concerned with the application of formal theories of probability and utility to the guidance of action. Decision analysis has been used for many years as a way to gain insight regarding decisions that involve significant amounts of uncertain information and complex ..."
Abstract

Cited by 5 (4 self)
 Add to MetaCart
The field of decision analysis is concerned with the application of formal theories of probability and utility to the guidance of action. Decision analysis has been used for many years as a way to gain insight regarding decisions that involve significant amounts of uncertain information and complex preference issues, but it has been largely overlooked by knowledgebased system researchers. This paper illustrates the value of incorporating decision analysis insights and techniques into the knowledge acquisition and decision making process. This approach is being implemented within Aquinas, an automated knowledge acquisition and decision support tool based on personal construct theory that is under development at Boeing Computer Services. The need for explicit preference models in knowledgebased systems will be shown. The modeling of problems will be viewed from the perspectives of decision analysis and personal construct theory. We will outline the approach of Aquinas and then present an example that illustrates how preferences can be used to guide the knowledge acquisition process and the selection of alternatives in decision making. Techniques for combining supervised and unsupervised inductive learning from data with expert judgment, and integration of knowledge and inference methods at varying levels of precision will be presented. Personal construct theory and decision theory are shown to be complementary: the former provides a plausible account of the dynamics of model formulation and revision, while the latter provides a consistent framework for model evaluation. Applied personal construct theory (in the form of tools such as Aquinas) and applied decision theory (in the form of decision analysis) are moving along convergent paths. We see the approach in this paper as the first step toward a full integration of insights from the two disciplines and their respective repertory grid and influence diagram representations.