Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
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28a7602724ba16600d5ccc644c19bf18-Supplemental.pdf
Neural Information Processing Systems
We first generalize existing algebraic characterizations of acyclicity to a class of matrix polynomials.
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28a7602724ba16600d5ccc644c19bf18-Paper.pdf
Neural Information Processing Systems
We first generalize existing algebraic characterizations of acyclicity to a class of matrix polynomials.
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RecursiveBayesianNetworks: GeneralisingandUnifying ProbabilisticContext-FreeGrammarsand DynamicBayesianNetworks
Neural Information Processing Systems
However,DBNsonly allow for a fixed chain of Markov dependencies between time slices and cannot represent nested hierarchicalstructures.
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BidirectionalConvolutionalPoissonGamma DynamicalSystems
Neural Information Processing Systems
Incorporating the natural document-sentence-word structure into hierarchical Bayesian modeling, we propose convolutional Poisson gamma dynamical systems (PGDS) that introduce not only word-level probabilistic convolutions, but alsosentence-levelstochastic temporaltransitions.
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