Networks of probabilistic events in discrete time

Galán, Severino F.; Díez, F. J.

doi:10.1016/s0888-613x(02)00071-3

Cited by 26 publications

(24 citation statements)

References 7 publications

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“…In a family of nodes interacting through a canonical model, the number of required parameters grows linearly with the number of parents. The construction of NasoNet led us to developing temporal canonical models [11] within the NPEDT approach. There are nodes in NasoNet whose number of parents rises to ten.…”

Section: Discussionmentioning

confidence: 99%

“…A network of probabilistic events in discrete time (NPEDT) [11] is a Bayesian network for temporal reasoning that leads to less complex networks than those obtained from the formalism of dynamic Bayesian networks, for domains involving temporal fault diagnosis and prediction. Under the NPEDT approach, time is discretized, nodes are associated with events, and each value of a node represents the occurrence of an event at a particular instant.…”

Section: Probabilistic Temporal Reasoningmentioning

confidence: 99%

“…Therefore, each conditional probability table expresses the most probable delays between parent events and the corresponding child event. Two major advantages of an NPEDT are that this model is not restricted to Markovian processes, and that we can make use of different temporal noisy gates [11] that facilitate knowledge acquisition and representation. Temporal noisy gates (temporal noisy OR-gate, temporal noisy AND-gate, and others) constitute a generalization for temporal processes of traditional canonical models.…”

Section: Probabilistic Temporal Reasoningmentioning

confidence: 99%

“…As the causal relations in our domain are not instantaneous and, furthermore, the nodes in the network correspond to temporal events, we use the temporal noisy OR-gate [11] as a model of causal interaction in the network. The temporal noisy OR-gate represents the case in which the effect is present as soon as any of its causes provokes it to be present.…”

Section: Description Of the Modelmentioning

confidence: 99%

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NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time

Galán

Aguado

Díez

et al. 2002

Artificial Intelligence in Medicine

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The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Probabilistic Temporal Reasoningmentioning

confidence: 99%

Section: Probabilistic Temporal Reasoningmentioning

confidence: 99%

Section: Description Of the Modelmentioning

confidence: 99%

See 2 more Smart Citations

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time

Galán

Aguado

Díez

et al. 2002

Artificial Intelligence in Medicine

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“…In fact, this assumption concerns non-repairable systems where the failure of a component (i.e., the event) happens only once during the mission time. Temporal Nodes Bayesian Networks (TNBNs) (Arroyo-Figueroa and Sucar, 1999), the Net of Irreversible Events in Discrete Time (NIEDT) (Galan and Diez, 2000) and Networks of Probabilistic Events in Discrete Time (NPEDTs) (Galan and Diez, 2002) are classified as eventbased approaches. Nodelman et al (2002) propose Conditional Markovian Processes (CMPs) for the modeling of Markovian processes by the BN formalism; it represents the equivalent of CPD in a BN.…”

mentioning

confidence: 99%

Bayesian reliability models of Weibull systems: State of the art

Zaïdi

Bouamama

Tagina

2012

International Journal of Applied Mathematics and Computer Science

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In the reliability modeling field, we sometimes encounter systems with uncertain structures, and the use of fault trees and reliability diagrams is not possible. To overcome this problem, Bayesian approaches offer a considerable efficiency in this context. This paper introduces recent contributions in the field of reliability modeling with the Bayesian network approach. Bayesian reliability models are applied to systems with Weibull distribution of failure. To achieve the formulation of the reliability model, Bayesian estimation of Weibull parameters and the model's goodness-of-fit are evoked. The advantages of this modelling approach are presented in the case of systems with an unknown reliability structure, those with a common cause of failures and redundant ones. Finally, we raise the issue of the use of BNs in the fault diagnosis area.

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Efficient computation for the noisy MAX

Díez

Galán

2003

Int. J. Intell. Syst.

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Díez's algorithm for the noisy MAX is very efficient for polytrees, but when the network has loops, it has to be combined with local conditioning, a suboptimal propagation algorithm. Other algorithms, based on several factorizations of the conditional probability of the noisy MAX, are not as efficient for polytrees but can be combined with general propagation algorithms such as clustering or variable elimination, which are more efficient for networks with loops. In this article we propose a new factorization of the noisy MAX that amounts to Díez's algorithm in the case of polytrees and at the same time is more efficient than previous factorizations when combined with either variable elimination or clustering.

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Networks of probabilistic events in discrete time

Cited by 26 publications

References 7 publications

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time

NasoNet, modeling the spread of nasopharyngeal cancer with networks of probabilistic events in discrete time

Bayesian reliability models of Weibull systems: State of the art

Efficient computation for the noisy MAX

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