The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks

Beinlich, Ingo A.; Suermondt, Henri J.; Chavez, R. Martin; Cooper, Gregory F.

doi:10.1007/978-3-642-93437-7_28

Cited by 411 publications

(290 citation statements)

References 8 publications

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“…BNs have proven to be successful in various domains such as medicine [18,19] and bioinformatics [20][21][22][23]. However, classical BNs are not well equipped to deal with temporal information.…”

Section: Bayesian Networkmentioning

confidence: 99%

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

Hernández-Leal

Rios-Flores

Ávila‐Ríos

et al. 2013

Artificial Intelligence in Medicine

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Section: Bayesian Networkmentioning

confidence: 99%

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

Hernández-Leal

Rios-Flores

Ávila‐Ríos

et al. 2013

Artificial Intelligence in Medicine

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“…In order to test the behavior of the methods proposed in the paper, we have selected the ALARM network [3]. This network has 37 nodes and 46 arcs and is used for diagnosis in a medical domain.…”

Section: Experimental Evaluation Of the Algorithmsmentioning

confidence: 99%

Stochastic Local Algorithms for Learning Belief Networks: Searching in the Space of the Orderings

Campos

Puerta

2001

Lecture Notes in Computer Science

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Abstract. An important type of methods for learning belief networks from data are those based on the use of a scoring metric, to evaluate the fitness of any given candidate network to the data base, and a search procedure to explore the set of candidate networks. In this paper we propose a new method that carries out the search not in the space of directed acyclic graphs but in the space of the orderings of the variables that compose the graphs. Moreover, we use a new stochastic search method to be applied to this problem, Variable Neighborhood Search. We also experimentally compare our methods with some other search procedures commonly used in the literature.

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“…For example, let us start from the network in the left hand side of Figure 1. 5 , x 6 } and θ 3 = {x 6 , x 3 , x 2 , x 4 , x 1 , x 5 } be three different orderings. If we apply the previous process, for θ 1 we recover the original graph, for θ 2 we obtain the second graph, and for the ordering θ 3 we get the much more dense graph on the right hand side of the same figure.…”

Section: Preliminariesmentioning

confidence: 99%

The Search of Causal Orderings: A Short Cut for Learning Belief Networks

Acid

Campos

Huete

2001

Lecture Notes in Computer Science

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Abstract. Although we can build a belief network starting from any ordering of its variables, its structure depends heavily on the ordering being selected: the topology of the network, and therefore the number of conditional independence relationships that may be explicitly represented can vary greatly from one ordering to another. We develop an algorithm for learning belief networks composed of two main subprocesses: (a) an algorithm that estimates a causal ordering and (b) an algorithm for learning a belief network given the previous ordering, each one working over different search spaces, the ordering and dag space respectively.

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The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks

Cited by 411 publications

References 8 publications

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

Discovering human immunodeficiency virus mutational pathways using temporal Bayesian networks

Stochastic Local Algorithms for Learning Belief Networks: Searching in the Space of the Orderings

The Search of Causal Orderings: A Short Cut for Learning Belief Networks

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