Results 11  20
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185
A Bayesian Approach to Causal Discovery
, 1997
"... We examine the Bayesian approach to the discovery of directed acyclic causal models and compare it to the constraintbased approach. Both approaches rely on the Causal Markov assumption, but the two differ significantly in theory and practice. An important difference between the approaches is that t ..."
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Cited by 85 (1 self)
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We examine the Bayesian approach to the discovery of directed acyclic causal models and compare it to the constraintbased approach. Both approaches rely on the Causal Markov assumption, but the two differ significantly in theory and practice. An important difference between the approaches is that the constraintbased approach uses categorical information about conditionalindependence constraints in the domain, whereas the Bayesian approach weighs the degree to which such constraints hold. As a result, the Bayesian approach has three distinct advantages over its constraintbased counterpart. One, conclusions derived from the Bayesian approach are not susceptible to incorrect categorical decisions about independence facts that can occur with data sets of finite size. Two, using the Bayesian approach, finer distinctions among model structuresboth quantitative and qualitativecan be made. Three, information from several models can be combined to make better inferences and to better ...
An experimental comparison of several clustering and intialization methods
, 1998
"... We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation–Maximization (EM) algorithm, a “winner take all ” version of the EM algorithm reminiscent of the Kmeans algorithm, a ..."
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Cited by 84 (1 self)
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We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectation–Maximization (EM) algorithm, a “winner take all ” version of the EM algorithm reminiscent of the Kmeans algorithm, and modelbased hierarchical agglomerative clustering. We learn naiveBayes models with a hidden root node, using highdimensional discretevariable data sets (both real and synthetic). We find that the EM algorithm significantly outperforms the other methods, and proceed to investigate the effect of various initialization schemes on the final solution produced by the EM algorithm. The initializations that we consider are (1) parameters sampled from an uninformative prior, (2) random perturbations of the marginal distribution of the data, and (3) the output of hierarchical agglomerative clustering. Although the methods are substantially different, they lead to learned models that are strikingly similar in quality. 1
Model selection for probabilistic clustering using crossvalidated likelihood
 Statistics and Computing
, 2000
"... ..."
Learning Bayesian network classifiers by maximizing conditional likelihood
 In ICML2004
, 2004
"... Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when learned in the standard way. This is attributable to a mismatch between the objective function used (likelihood or a function ..."
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Cited by 67 (0 self)
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Bayesian networks are a powerful probabilistic representation, and their use for classification has received considerable attention. However, they tend to perform poorly when learned in the standard way. This is attributable to a mismatch between the objective function used (likelihood or a function thereof) and the goal of classification (maximizing accuracy or conditional likelihood). Unfortunately, the computational cost of optimizing structure and parameters for conditional likelihood is prohibitive. In this paper we show that a simple approximation— choosing structures by maximizing conditional likelihood while setting parameters by maximum likelihood—yields good results. On a large suite of benchmark datasets, this approach produces better class probability estimates than naive Bayes, TAN, and generativelytrained Bayesian networks. 1.
Clustering using Monte Carlo CrossValidation
, 1996
"... Finding the "right" number of clusters, k, for a data set is a difficult, and often illposed, problem. In a probabilistic clustering context, likelihoodratios, penalized likelihoods, and Bayesian techniques are among the more popular techniques. In this paper a new crossvalidated likeli ..."
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Cited by 65 (0 self)
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Finding the "right" number of clusters, k, for a data set is a difficult, and often illposed, problem. In a probabilistic clustering context, likelihoodratios, penalized likelihoods, and Bayesian techniques are among the more popular techniques. In this paper a new crossvalidated likelihood criterion is investigated for determining cluster structure. A practical clustering algorithm based on Monte Carlo crossvalidation (MCCV) is introduced. The algorithm permits the data analyst to judge if there is strong evidence for a particular k, or perhaps weaker evidence over a subrange of k values. Experimental results with Gaussian mixtures on real and simulated data suggest that MCCV provides genuine insight into cluster structure. vfold crossvalidation appears inferior to the penalized likelihood method (BIC), a Bayesian algorithm (AutoClass v2.0), and the new MCCV algorithm. Overall, MCCV and AutoClass appear the most reliable of the methods. MCCV provides the dataminer with a usefu...
Multiple Regimes in Northern Hemisphere Height Fields via Mixture Model Clustering
 J. Atmos. Sci
, 1998
"... Mixture model clustering is applied to Northern Hemisphere (NH) 700mb geopotential height anomalies. A mixture model is a flexible probability density estimation technique, consisting of a linear combination of k component densities. A key feature of the mixture modeling approach to clustering is t ..."
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Cited by 57 (31 self)
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Mixture model clustering is applied to Northern Hemisphere (NH) 700mb geopotential height anomalies. A mixture model is a flexible probability density estimation technique, consisting of a linear combination of k component densities. A key feature of the mixture modeling approach to clustering is the ability to estimate a posterior probability distribution for k, the number of clusters, given the data and the model, and thus objectively determine the number of clusters that is most likely to fit the data. A data set of 44 winters of NH 700mb fields is projected onto its two leading empirical orthogonal functions (EOFs) and analyzed using mixtures of Gaussian components. Crossvalidated likelihood is used to determine the best value of k, the number of clusters. The posterior probability so determined peaks at k = 3 and thus yields clear evidence for 3 clusters in the NH 700mb data. The 3cluster result is found to be robust with respect to variations in data preprocessing and data an...
ContextSpecific Bayesian Clustering for Gene Expression Data
, 2002
"... The recent growth in genomic data and measurements of genomewide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors. ..."
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Cited by 56 (5 self)
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The recent growth in genomic data and measurements of genomewide expression patterns allows us to apply computational tools to examine gene regulation by transcription factors.
Variable Selection for ModelBased Clustering
 Journal of the American Statistical Association
, 2006
"... We consider the problem of variable or feature selection for modelbased clustering. We recast the problem of comparing two nested subsets of variables as a model comparison problem, and address it using approximate Bayes factors. We develop a greedy search algorithm for finding a local optimum in m ..."
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Cited by 53 (5 self)
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We consider the problem of variable or feature selection for modelbased clustering. We recast the problem of comparing two nested subsets of variables as a model comparison problem, and address it using approximate Bayes factors. We develop a greedy search algorithm for finding a local optimum in model space. The resulting method selects variables (or features), the number of clusters, and the clustering model simultaneously. We applied the method to several simulated and real examples, and found that removing irrelevant variables often improved performance. Compared to methods based on all the variables, our variable selection method consistently yielded more accurate estimates of the number of clusters, and lower classification error rates, as well as more parsimonious clustering models and easier visualization of results.
Hierarchical Latent Class Models for Cluster Analysis
 Journal of Machine Learning Research
, 2002
"... Latent class models are used for cluster analysis of categorical data. Underlying such a model is the assumption that the observed variables are mutually independent given the class variable. A serious problem with the use of latent class models, known as local dependence, is that this assumption is ..."
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Cited by 53 (12 self)
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Latent class models are used for cluster analysis of categorical data. Underlying such a model is the assumption that the observed variables are mutually independent given the class variable. A serious problem with the use of latent class models, known as local dependence, is that this assumption is often untrue. In this paper we propose hierarchical latent class models as a framework where the local dependence problem can be addressed in a principled manner. We develop a searchbased algorithm for learning hierarchical latent class models from data. The algorithm is evaluated using both synthetic and realworld data.
Asymptotic model selection for directed networks with hidden variables
, 1996
"... We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large samples of data. The standard BIC as well as our extension punishes the complexity of a ..."
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Cited by 49 (15 self)
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We extend the Bayesian Information Criterion (BIC), an asymptotic approximation for the marginal likelihood, to Bayesian networks with hidden variables. This approximation can be used to select models given large samples of data. The standard BIC as well as our extension punishes the complexity of a model according to the dimension of its parameters. We argue that the dimension of a Bayesian network with hidden variables is the rank of the Jacobian matrix of the transformation between the parameters of the network and the parameters of the observable variables. We compute the dimensions of several networks including the naive Bayes model with a hidden root node. 1