The variational gaussian approximation revisited
作者： Manfred Opper , Cédric Archambeau
DOI: 10.1162/NECO.2008.08-07-592
关键词:
摘要: The variational approximation of posterior distributions by multivariate gaussians has been much less popular in the machine learning community compared to corresponding factorizing distributions. This is for a good reason: gaussian general plagued an number parameters be optimized, being random variables. In this letter, we discuss relationship between Laplace and approximation, show that models with priors likelihoods, actually . approach applied process regression nongaussian likelihoods.
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