iterLap: Approximate Probability Densities by Iterated Laplace Approximations

The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly non-normalized) probability density on R^p, by repeated Laplace approximations to the difference between current approximation and true density (on log scale). The final approximation is a mixture of multivariate normal distributions and might be used for example as a proposal distribution for importance sampling (eg in Bayesian applications). The algorithm can be seen as a computational generalization of the Laplace approximation suitable for skew or multimodal densities.

Package details

AuthorBjoern Bornkamp
MaintainerBjoern Bornkamp <>
Package repositoryView on CRAN
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iterLap documentation built on May 2, 2019, 2:11 p.m.