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Directed graphical models
, 2006
"... * Denotes advanced sections that may be omitted on a first reading. ..."
1 Graphical models Directed graphical models
, 2007
"... We have already seen how conditional independence (CI) assumptions help to represent joint distributions in terms of smaller pieces (see the chapter on naive Bayes classifiers, Chapter??). We give a very simple example in Figure 1, where we show how the joint distribution p(X, Y) can be represented ..."
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in terms of the marginal distributions p(X) and p(Y) if we assume that X and Y are (unconditionally) independent, X ⊥ Y. Graphical models provide a way to represent CI assumptions pictorially, in terms of graph (network) structures. The nodes represent random variables, and the (lack of) edges represent CI
Learning Extensible MultiEntity Directed Graphical Models
"... Graphical models have become a standard tool for representing complex probability models in statistics and artificial intelligence. In problems arising in artificial intelligence, it is useful to use the belief network formalism to represent uncertain relationships among variables in the domai ..."
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Graphical models have become a standard tool for representing complex probability models in statistics and artificial intelligence. In problems arising in artificial intelligence, it is useful to use the belief network formalism to represent uncertain relationships among variables
Algebraic Methods of Classifying Directed Graphical Models
"... In information theory, structural system constraints are frequently described in the form of a directed acyclic graphical models (DAG). This paper addresses the question of classifying DAGs up to an isomorphism. By considering Gaussian densities, question reduces to verifying equality of certain alg ..."
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In information theory, structural system constraints are frequently described in the form of a directed acyclic graphical models (DAG). This paper addresses the question of classifying DAGs up to an isomorphism. By considering Gaussian densities, question reduces to verifying equality of certain
directed graphical models with hidden variables. Bayesian
"... Appendix 1: sample size requirements for instrumental variable analysis with genetic instruments For given Type 1 and Type 2 error probabilities and study design, the sample size required to detect an effect of size d is proportional to 1/d2V, where V is the Fisher information (expectation of minus ..."
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of minus the second derivative of the loglikelihood of the effect size) from a single observation. For a single observation from a logistic regression model (in which the effect is measured as the log OR), the Fisher information is f(1f)v, where f is the probability of being a case, and v
Contents lists available at ScienceDirect Graphical Models
"... journal homepage: www.elsevier.com/locate/gmod Thinning combined with iterationbyiteration smoothing ..."
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journal homepage: www.elsevier.com/locate/gmod Thinning combined with iterationbyiteration smoothing
Contents lists available at ScienceDirect Graphical Models
"... journal homepage: www.elsevier.com/locate/gmod ..."
Contents lists available at ScienceDirect Graphical Models
"... journal homepage: www.elsevier.com/locate/gmod ..."
Directed Graphical Models Of Classifier Combination: Application To Phone Recognition
 In Proc. Int. Conf. on Spoken Language Processing
, 2000
"... Classifier combination is a technique that often provides appreciable accuracy gains. In this paper, we argue that the underlying statistical model of classifier combination should be made explicit. Using directed graphical models (DGMs), we provide representations of two common combination schemes, ..."
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Cited by 11 (2 self)
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Classifier combination is a technique that often provides appreciable accuracy gains. In this paper, we argue that the underlying statistical model of classifier combination should be made explicit. Using directed graphical models (DGMs), we provide representations of two common combination schemes
An introduction to variational methods for graphical models
 TO APPEAR: M. I. JORDAN, (ED.), LEARNING IN GRAPHICAL MODELS
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