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Concave Gaussian Variational Approximations for Inference in Large-Scale Bayesian Linear Models
Edward Challis
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Oznake: computer science;machine learning;bayesian learning;gaussian processes
Two popular approaches to forming principled bounds in approximate Bayesian inference are local variational methods and minimal Kullback-Leibler divergence methods. For a large class of models, we explicitly relate the two approaches, showing that the local variational method is equivalent to a weak ...
Leto: 2011 Vir: videolectures.net
Št. zadetkov: 1
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