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We propose a novel distributed inference algorithm for continuous graphical models, by extending Stein variational gradient descent (SVGD) to leverage the Markov dependency structure of the distribution of interest.
On the distinction between the conditional probability and the joint probability approaches in the specification of nearest-neighbour systems
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Stein variational gradient descent: A general purpose Bayesian inference algorithm
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A kernelized Stein discrepancy for goodness-of-fit tests
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Convergence rates for a class of estimators based on Stein’s identity
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Efficient observation selection in probabilistic graphical models using Bayesian lower bounds
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