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32
Theory refinement of bayesian networks with hidden variables
 In Machine Learning: Proceedingsof the International Conference
, 1998
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Parameter Learning in Object Oriented Bayesian Networks
, 2001
"... This paper describes a method for parameter learning in ObjectOriented Bayesian Networks (OOBNs). We propose a methodology for learning parameters in OOBNs, and prove that maintaining the object orientation imposed by the prior model will increase the learning speed in objectoriented domains. We a ..."
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This paper describes a method for parameter learning in ObjectOriented Bayesian Networks (OOBNs). We propose a methodology for learning parameters in OOBNs, and prove that maintaining the object orientation imposed by the prior model will increase the learning speed in objectoriented domains. We also propose a method to efficiently estimate the probability parameters in domains that are not strictly object oriented. Finally, we attack type uncertainty, a special case of model uncertainty typical to objectoriented domains
Score and Information for Recursive Exponential Models with Incomplete Data.
"... Recursive graphical models usually underlie the statistical modelling concerning probabilistic expert systems based on Bayesian networks. This paper defines a version of these models, denoted as recursive exponential models, which have evolved by the desire to impose sophisticated domain knowl ..."
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Recursive graphical models usually underlie the statistical modelling concerning probabilistic expert systems based on Bayesian networks. This paper defines a version of these models, denoted as recursive exponential models, which have evolved by the desire to impose sophisticated domain knowledge onto local fragments of a model. Besides the structural knowledge, as specified by a given model, the statistical modelling may also include expert opinion about the values of parameters in the model. It is shown how to translate imprecise expert knowledge into approximately conjugate prior distributions. Based on possibly incomplete data, the score and the observed information are derived for these models. This accounts for both the traditional score and observed information, derived as derivatives of the loglikelihood, and the posterior score and observed information, derived as derivatives of the logposterior distribution. Throughout the paper the specialization int...
Learning hybrid Bayesian networks from data
, 1998
"... We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first method ..."
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Cited by 11 (1 self)
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We illustrate two different methodologies for learning Hybrid Bayesian networks, that is, Bayesian networks containing both continuous and discrete variables, from data. The two methodologies differ in the way of handling continuous data when learning the Bayesian network structure. The first methodology uses discretized data to learn the Bayesian network structure, and the original nondiscretized data for the parameterization of the learned structure. The second methodology uses nondiscretized data both to learn the Bayesian network structure and its parameterization. For the direct handling of continuous data, we propose the use of artificial neural networks as probability estimators, to be used as an integral part of the scoring metric defined to search the space of Bayesian network structures. With both methodologies, we assume the availability of a complete dataset, with no missing values or hidden variables. We report experimental results aimed at comparing the two methodologies. These results provide evidence that learning with discretized data presents advantages both in terms of efficiency and in terms of accuracy of the learned models over the alternative approach of using nondiscretized data.
Accelerating EM: An Empirical Study
 IN PROCEEDINGS OF THE FIFTEENTH ANNUAL CONFERENCE ON UNCERTAINTY IN ARTICIAL INTELLIGENCE (UAI99
, 1999
"... Many applications require that we learn the parameters of a model from data. EM (ExpectationMaximization) is a method for learning the parameters of probabilistic models with missing or hidden data. There are instances in which this method is slow to converge. Therefore, several accelerations ..."
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Many applications require that we learn the parameters of a model from data. EM (ExpectationMaximization) is a method for learning the parameters of probabilistic models with missing or hidden data. There are instances in which this method is slow to converge. Therefore, several accelerations have been proposed to improve the method. None of the proposed acceleration methods are theoretically dominant and experimental comparisons are lacking. In this paper, we present the different proposed accelerations and compare them experimentally. From the results of the experiments, we argue that some acceleration of EM is always possible, but that which acceleration is superior depends on properties of the problem.
Learning Conditional Probabilities from Incomplete Data: An Experimental Comparison
 In: Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics
, 1999
"... This paper compares three methods  em algorithm, Gibbs sampling, and Bound and Collapse (bc)  to estimate conditional probabilities from incomplete databases in a controlled experiment. Results show a substantial equivalence of the estimates provided by the three methods and a dramatic gain in ..."
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This paper compares three methods  em algorithm, Gibbs sampling, and Bound and Collapse (bc)  to estimate conditional probabilities from incomplete databases in a controlled experiment. Results show a substantial equivalence of the estimates provided by the three methods and a dramatic gain in efficiency using bc. Reprinted from: Proceedings of Uncertainty 99: Seventh International Workshop on Artificial Intelligence and Statistics, Morgan Kaufmann, San Mateo, CA, 1999. Address: Marco Ramoni, Knowledge Media Institute, The Open University, Milton Keynes, United Kingdom MK7 6AA. phone: +44 (1908) 655721, fax: +44 (1908) 653169, email: m.ramoni@open.ac.uk, url: http://kmi.open.ac.uk/people/marco. Learning Conditional Probabilities from Incomplete Data: An Experimental Comparison Marco Ramoni Knowledge Media Institute The Open University Paola Sebastiani Statistics Department The Open University Abstract This paper compares three methods  em algorithm, Gibbs sampling, an...
On the Choice of the Number of Blocks with the Incremental EM Algorithm for the Fitting of Normal Mixtures
, 2003
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Learning Bayesian Network Parameters Under Incomplete Data with Domain Knowledge
"... Bayesian networks have gained increasing attention in recent years. One key issue in Bayesian networks (BNs) is parameter learning. When training data is incomplete or sparse or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. Under these ..."
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Bayesian networks have gained increasing attention in recent years. One key issue in Bayesian networks (BNs) is parameter learning. When training data is incomplete or sparse or when multiple hidden nodes exist, learning parameters in Bayesian networks (BNs) becomes extremely difficult. Under these circumstances, the learning algorithms are required to operate in a highdimensional search space and they could easily get trapped among copious local maxima. This paper presents a learning algorithm to incorporate domain knowledge into the learning to regularize the otherwise illposed problem, to limit the search space, and to avoid local optima. Unlike the conventional approaches that typically exploit the quantitative domain knowledge such as prior probability distribution, our method systematically incorporates qualitative constraints on some of the parameters into the learning process. Specifically, the problem is formulated as a constrained optimization problem, where an objective function is defined as a combination of the likelihood function and penalty functions constructed from the qualitative domain knowledge. Then, a gradientdescent procedure is systematically integrated with the Estep and Mstep of the EM algorithm, to estimate the parameters iterativelyuntil it converges. The experiments with both synthetic data and real data for facial action recognition show 2 our algorithm improves the accuracy of the learned BN parameters significantly over the conventional EM algorithm. I.
Bayesian Networks for Genomic Analysis
, 2004
"... Bayesian networks are emerging into the genomic arena as a general modeling tool able to unravel the cellular mechanism, to identify genotypes that confer susceptibility to disease, and to lead to diagnostic models. This chapter reviews the foundations of Bayesian networks and shows their applicatio ..."
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Bayesian networks are emerging into the genomic arena as a general modeling tool able to unravel the cellular mechanism, to identify genotypes that confer susceptibility to disease, and to lead to diagnostic models. This chapter reviews the foundations of Bayesian networks and shows their application to the analysis of various types of genomic data, from genomic markers to gene expression data. The examples will highlight the potential of this methodology but also the current limitations and we will describe new research directions that hold the promise to make Bayesian networks a fundamental tool for genome data
Learning Naive Bayes Classifier from Noisy Data
, 2003
"... Classification is one of the major tasks in knowledge discovery and data mining. Naive Bayes classifier, in spite of its simplicity, has proven surprisingly effective in many practical applications. In real datasets, noise is inevitable, because of the imprecision of measurement or privacy preservin ..."
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Classification is one of the major tasks in knowledge discovery and data mining. Naive Bayes classifier, in spite of its simplicity, has proven surprisingly effective in many practical applications. In real datasets, noise is inevitable, because of the imprecision of measurement or privacy preserving mechanisms. In this paper, we develop a new approach, LinEarEquationbased noiseaWare bAYes classifier (LEEWAY), for learning the underlying naive Bayes classifier from noisy observations. Using