A Munin Network for the Median Nerve-a Case Study on Loops

Olesen, Kristian; Kjærulff, Uffe; Jensen, Frank; Falck, Björn; Andreassen, Steen; Andersen, Stig Kjær

doi:10.1080/08839518908949933

Cited by 89 publications

(40 citation statements)

References 8 publications

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“…One way to simplify obtaining probabilistic information is to reduce the number of parents of a given vertex in a causal graph. This may be done by introducing 'summarizing' intermediate vertices, a technique known as 'divorcing multiple parents' [19]. The main advantage of this technique is that conditional probabilities…”

Section: Sources Of Probabilistic Informationmentioning

confidence: 99%

Converting a rule-based expert system into a belief network

Korver

Lucas

1993

Medical Informatics

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The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledge-based (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rule-based expert systems, the theory of belief networks is mathematically sound, based on techniques from probability theory. It therefore seems attractive to convert existing rule-based expert systems into belief networks. In this article, we discuss the design of a belief network reformulation of the diagnostic rule-based expert system HEPAR. For the purpose of this experiment, we have studied several typical pieces of medical knowledge represented in the HEPAR system. It turned out that, due to the differences in the type of knowledge represented and in the formalism used to represent uncertainty, much of the medical knowledge required for building the belief network concerned could not be extracted from HEPAR. As a consequence, significant additional knowledge acquisition was required. However, the objects and attributes defined in the HEPAR system, as well as the conditions in production rules mentioning these objects and attributes were useful for guiding the selection of the statistical variables for building the belief network. The mapping of objects and attributes in HEPAR to statistical variables is discussed in detail.

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Section: Sources Of Probabilistic Informationmentioning

confidence: 99%

Converting a rule-based expert system into a belief network

Korver

Lucas

1993

Medical Informatics

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“…In order to test the behaviour of the factorisation techniques proposed in this paper when applied to distributions coming from real world domains, we have considered the task of computing the posterior distribution of the variables in two Bayesian networks that describe actual scenarios, that we will denote as Munin1 [13] and Link [11]. We have represented the distributions in both networks as probability trees, and carried out the calculations using the so-called Lazy-penniless architecture [7], modified in such a way that the probability trees are factorised using both methods of approximate factorisation with average free term, AF and VF.…”

Section: Experiments With Real World Problemsmentioning

confidence: 99%

New strategies for finding multiplicative decompositions of probability trees

Martínez

Moral

Rodríguez

et al. 2013

Applied Mathematics and Computation

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Probability trees are a powerful data structure for representing probabilistic potentials. However, their complexity can become intractable if they represent a probability distribution over a large set of variables. In this paper, we study the problem of decomposing a probability tree as a product of smaller trees, with the aim of being able to handle bigger probabilistic potentials. We propose exact and approximate approaches and evaluate their behaviour through an extensive set of experiments. * Corresponding author

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“…Two well known approaches to the decomposition are: parent divorcing [12] and temporal transformation [6]. Parent divorcing constructs a binary tree in which each node encodes the binary operator.…”

Section: Decomposition Of a Max Querymentioning

confidence: 99%

A Bayesian approach for on-line max and min auditing

Cavallo

2008

Proceedings of the 2008 International Workshop on Privacy and Anonymity in Information Society

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In this paper we consider the on-line max and min query auditing problem: given a private association between fields in a data set, a sequence of max and min queries that have already been posed about the data, their corresponding answers and a new query, deny the answer if a private information is inferred or give the true answer otherwise. We give a probabilistic definition of privacy and demonstrate that max and min queries, without "no duplicates"assumption, can be audited by means of a Bayesian network. Moreover, we show how our auditing approach is able to manage user prior-knowledge.

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A Munin Network for the Median Nerve-a Case Study on Loops

Cited by 89 publications

References 8 publications

Converting a rule-based expert system into a belief network

Converting a rule-based expert system into a belief network

New strategies for finding multiplicative decompositions of probability trees

A Bayesian approach for on-line max and min auditing

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