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iterLap (version 1.1-2)

Approximate probability densities by iterated Laplace Approximations

Description

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.

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Install

install.packages('iterLap')

Monthly Downloads

263

Version

1.1-2

License

GPL

Maintainer

Last Published

May 22nd, 2012

Functions in iterLap (1.1-2)