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作者：Geiger, D; Heckerman, D
作者单位：Technion Israel Institute of Technology; Microsoft
摘要：We develop simple methods for constructing parameter priors for model choice among directed acyclic graphical (DAG) models. In particular, we introduce several assumptions that permit the construction of parameter priors for a large number of DAG models from a small set of assessments. We then present a method for directly computing the marginal likelihood of every DAG model given a random sample with no missing observations. We apply this methodology to Gaussian DAG models which consist of a ...
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作者：Anderson, TW
作者单位：Stanford University
摘要：When the rank of the autoregression matrix is unrestricted, the maximum likelihood estimator under normality is the least squares estimator. When the rank is restricted, the maximum likelihood estimator is composed of the eigenvectors of the effect covariance matrix in the metric of the error covariance matrix corresponding to the largest eigenvalues [Anderson, T. W. (1951). Ann. Math. Statist. 22 327-351]. The asymptotic distribution of these two covariance matrices under normality is obtaine...
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作者：Richardson, T; Spirtes, P
作者单位：University of Washington; University of Washington Seattle; Florida Institute for Human & Machine Cognition (IHMC)
摘要：This paper introduces a class of graphical independence models that is closed under marginalization and conditioning but that contains all DAG independence models. This class of graphs, called maximal ancestral graphs, has two attractive features: there is at most one edge between each pair of vertices; every missing edge corresponds to an independence relation, These features lead to a simple parameterization of the corresponding set of distributions in the Gaussian case.
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作者：Tsybakov, AB
作者单位：Universite Paris Cite; Sorbonne Universite
