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Inference and Learning in Hybrid Bayesian Networks
, 1998
"... We survey the literature on methods for inference and learning in Bayesian Networks composed of discrete and continuous nodes, in which the continuous nodes have a multivariate Gaussian distribution, whose mean and variance depends on the values of the discrete nodes. We also briefly consider hybrid ..."
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We survey the literature on methods for inference and learning in Bayesian Networks composed of discrete and continuous nodes, in which the continuous nodes have a multivariate Gaussian distribution, whose mean and variance depends on the values of the discrete nodes. We also briefly consider hybrid Dynamic Bayesian Networks, an extension of switching Kalman filters. This report is meant to summarize what is known at a sufficient level of detail to enable someone to implement the algorithms, but without dwelling on formalities.
Latent classification models
 Machine Learning
, 2005
"... Abstract. One of the simplest, and yet most consistently wellperforming set of classifiers is the Naïve Bayes models. These models rely on two assumptions: (i) All the attributes used to describe an instance are conditionally independent given the class of that instance, and (ii) all attributes fol ..."
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Abstract. One of the simplest, and yet most consistently wellperforming set of classifiers is the Naïve Bayes models. These models rely on two assumptions: (i) All the attributes used to describe an instance are conditionally independent given the class of that instance, and (ii) all attributes follow a specific parametric family of distributions. In this paper we propose a new set of models for classification in continuous domains, termed latent classification models. The latent classification model can roughly be seen as combining the Naïve Bayes model with a mixture of factor analyzers, thereby relaxing the assumptions of the Naïve Bayes classifier. In the proposed model the continuous attributes are described by a mixture of multivariate Gaussians, where the conditional dependencies among the attributes are encoded using latent variables. We present algorithms for learning both the parameters and the structure of a latent classification model, and we demonstrate empirically that the accuracy of the proposed model is significantly higher than the accuracy of other probabilistic classifiers. Keywords: classification, probabilistic graphical models, Naïve Bayes, correlation
Control of Dynamic Systems Using Bayesian Networks
 Proceedings of the IBERAMIA/SBIA 2000 Workshops (Atibaia, Sdo Paulo
, 2000
"... Bayesian networks for the static as well as for the dynamic case have gained an enormous interest in the research community of artificial intelligence, machine learning and pattern recognition. Although the parallels between dynamic Bayesian networks and Kalman filters are well known since many ye ..."
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Bayesian networks for the static as well as for the dynamic case have gained an enormous interest in the research community of artificial intelligence, machine learning and pattern recognition. Although the parallels between dynamic Bayesian networks and Kalman filters are well known since many years, Bayesian networks have not been applied to problems in the area of adaptive control of dynamic systems.
DIAGNOSIS METHOD FOR SPACECRAFT USING DYNAMIC BAYESIAN NETWORKS
"... Development of sophisticated anomaly detection and diagnosis methods for spacecraft is one of the important problems in space system operation. In this study, we propose a diagnosis method for spacecraft using probabilistic reasoning and statistical learning with Dynamic Bayesian Networks (DBNs). In ..."
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Development of sophisticated anomaly detection and diagnosis methods for spacecraft is one of the important problems in space system operation. In this study, we propose a diagnosis method for spacecraft using probabilistic reasoning and statistical learning with Dynamic Bayesian Networks (DBNs). In this method, the DBNs are initially from priorknowledge, then modified or partly reconstructed by statistical learning with operation data, as a result adaptable and indepth diagnosis is performed by probabilistic reasoning using the DBNs. The proposed method was applied to the telemetry data that simulates the malfunction of thrusters in rendezvous maneuver of spacecraft, and the effectiveness of the method was confirmed.
Proposed design for gR, a graphical models toolkit for R
, 2003
"... This document is an extension of the talk I gave at the gR meeting in Aalborg on 19 September 2003. It outlines a proposed design for gR, a graphical models library for R. This design is similar to the design of BNT, but is much more general, in that it supports undirected models and chain graphs, a ..."
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This document is an extension of the talk I gave at the gR meeting in Aalborg on 19 September 2003. It outlines a proposed design for gR, a graphical models library for R. This design is similar to the design of BNT, but is much more general, in that it supports undirected models and chain graphs, and allows parameters to be represented as random variables (Bayesian modeling).