KNET: Integrating Hypermedia and Normative Bayesian Modeling

Chavez, R. Martin; Cooper, Gregory F.

doi:10.1016/b978-0-444-88650-7.50031-7

Cited by 12 publications

(8 citation statements)

References 6 publications

(3 reference statements)

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“…The feasibility of using belief networks in constructing diagnostic systems has been demonstrated in several domains (Agogino & Rege, 1987;Andreassen, Woldbye, Falck, & Andersen, 1987;Beinlich, Suermondt, Chavez, & Cooper, 1989;Chavez & Cooper, 1990;Cooper, 1984;Heckerman, Horvitz, & Nathwani, 1989;Henrion & Cooley, 1987;Holtzman, 1989;Suermondt & Amylon, 1989).…”

Section: X~) = I~ P(x~ [ ~R~) (1) I=1mentioning

confidence: 99%

A Bayesian method for the induction of probabilistic networks from data

Cooper¹,

Herskovits²

1992

Mach Learn

Self Cite

2,446

2,038

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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 computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabifistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probahilistic 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.Keywords. probabilistic networks, Bayesian belief networks, machine learning, induction I n t r o d u c t i o nIn this paper, we present a Bayesian method for constructing a probabilistic network from a database of records, which we call cases. Once constructed, such a network can provide insight into probabilistic dependencies that exist among the variables in the database. One application is the automated discovery of dependency relationships. The computer program searches for a probabilistic-network structure that has a high posterior probability given the database, and outputs the structure and its probability. A related task is computer-assisted hypothesis testing: The user enters a hypothetical structure of the dependency relationships among a set of variables, and the program calculates the probability of the structure given a database of cases on the variables.We can also construct a network and use it for computer-based diagnosis. For example, suppose we have a database in which a case contains data about the behavior of some system (i.e., findings). Suppose further that a case contains data about whether this particular behavior follows from proper system operation, or alternatively, is caused by one of several possible faults. Assume that the database contains many such cases from previous episodes of proper and faulty behavior. The method that we present in this paper can be used to construct from the database a probabilistic network that captures the probabilistic dependencies among findings and faults. Such a network then can be applied to classify future cases of system behavior by assigning a posterior probability to each of the possible faults and to the event "proper system operation." In this paper, we also shall discuss diagnostic inference that is based on combining the inferences of multiple alternative networks.

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Section: X~) = I~ P(x~ [ ~R~) (1) I=1mentioning

confidence: 99%

A Bayesian method for the induction of probabilistic networks from data

Cooper¹,

Herskovits²

1992

Mach Learn

Self Cite

2,446

2,038

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“…Аналіз останніх досліджень і публікацій. Запропонована у дослідженні технологія прогнозування впливу на ВВП варіантів фінансування закладів освіти України ґрунтується на дослідженнях таких зарубіжних учених: І. Бейнлиха [1], Є. Гершковича [3], М. Каяалпа, Г. Купера [2][3][4][5], Х. Сурмондта [1], Р. Чавеса [5] та інших. Ці вчені, використовуючи Байєсові моделі, які являють собою набір випадкових змінних та їхніх умовних залежностей за допомогою орієнтованого ациклічного графу, довели її результативність і можливість використання у багатьох галузях, у тому числі й економіці, для прогнозування і моделювання різноманітних процесів.…”

Section: The Article Uses Bayesian Network For Processing Statisticaunclassified

Technology of Forecasting the Impact on GDP of Options for Financing Educational Institutions of Ukraine

Zhiwei

2020

Scientific Opinion: Economics and Management

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“…The knowl edge acquisition system has capabilities similar to those found in other interactive tools of this kind (eg. KNET (Chavez & Cooper, 1988), and DAVID (Sha.chter, 1986)). It takes advantage of the net work structure to focus knowledge acquisition on a.…”

Section: A Bayesian Reasoning Toolmentioning

confidence: 99%

“…Because of these many advantages, it is not sur prising that software tools have been developed to make belief networks and related techniques more accessible to knowledge engineers ( eg. (Shachter, 1988), (Chavez & Cooper, 1988)). While these tools have already proven to be useful in many appli cations, this paper contends that they do not go far enough in meeting the requirements of current knowledge based systems.…”

Section: Introductionmentioning

confidence: 99%

BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems

Booker

Hota

Ramsey

1990

Uncertainty in Artificial Intelligence

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As the technology for building knowledge based systems has matured, important lessons have been learned about the relationship between the architec ture of a system and the nature of the problems it is intended to solve. We are implementing a knowledge engineering tool called BART that is designed with these lessons in mind. BART is a Bayesian reason ing tool that makes belief networks and other prob abilistic techniques available to knowledge engineers building classificatory problem solvers. BART has already been used to develop a decision aid for clas sifying ship images, and it is currently being used to manage uncertainty in systems concerned with analyzing intelligence reports. This paper discusses how state-of-the-art probabilistic methods fi t nat urally into a knowledge based approach to classifi catory problem solving, and describes the current capabilities of BART.

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KNET: Integrating Hypermedia and Normative Bayesian Modeling

Cited by 12 publications

References 6 publications

A Bayesian method for the induction of probabilistic networks from data

A Bayesian method for the induction of probabilistic networks from data

Technology of Forecasting the Impact on GDP of Options for Financing Educational Institutions of Ukraine

BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems

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