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.

Author | Bjoern Bornkamp |

Date of publication | 2012-05-22 20:52:08 |

Maintainer | Bjoern Bornkamp <bbnkmp@gmail.com> |

License | GPL |

Version | 1.1-2 |

iterLap

iterLap/MD5

iterLap/DESCRIPTION

iterLap/R

iterLap/R/iterLap.R
iterLap/inst

iterLap/inst/CITATION

iterLap/src

iterLap/src/iterLap.c

iterLap/NAMESPACE

iterLap/man

iterLap/man/iterLap-internal.Rd
iterLap/man/resample.Rd
iterLap/man/GRApprox.Rd
iterLap/man/iterLap.Rd
iterLap/man/ISandIMH.Rd
iterLap/man/iterLap-package.Rd
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