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• W3100197521 abstract "Variational methods are employed in situations where exact Bayesian inference becomes intractable due to the difficulty in performing certain integrals. Typically, variational methods postulate a tractable posterior and formulate a lower bound on the desired integral to be approximated, e.g. marginal likelihood. The lower bound is then optimised with respect to its free parameters, the so-called variational parameters. However, this is not always possible as for certain integrals it is very challenging (or tedious) to come up with a suitable lower bound. Here, we propose a simple scheme that overcomes some of the awkward cases where the usual variational treatment becomes difficult. The scheme relies on a rewriting of the lower bound on the model log-likelihood. We demonstrate the proposed scheme on a number of synthetic and real examples, as well as on a real geophysical model for which the standard variational approaches are inapplicable." @default.
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• W3100197521 date "2015-06-30" @default.
• W3100197521 modified "2023-10-11" @default.
• W3100197521 title "Approximate variational inference based on a finite sample of Gaussian latent variables" @default.
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• W3100197521 doi "https://doi.org/10.1007/s10044-015-0496-9" @default.
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