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JMLR
2010

Improving posterior marginal approximations in latent Gaussian models

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Improving posterior marginal approximations in latent Gaussian models
We consider the problem of correcting the posterior marginal approximations computed by expectation propagation and Laplace approximation in latent Gaussian models and propose correction methods that are similar in spirit to the Laplace approximation of Tierney and Kadane (1986). We show that in the case of sparse Gaussian models, the computational complexity of expectation propagation can be made comparable to that of the Laplace approximation by using a parallel updating scheme. In some cases, expectation propagation gives excellent estimates, where the Laplace approximation fails. Inspired by bounds on the marginal corrections, we arrive at factorized approximations, which can be applied on top of both expectation propagation and Laplace. These give nearly indistinguishable results from the non-factorized approximations in a fraction of the time.
Botond Cseke, Tom Heskes
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Botond Cseke, Tom Heskes
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