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.

Version: 1.1-2
Depends: quadprog, randtoolbox, parallel, R (≥ 2.15)
Published: 2012-05-22
Author: Bjoern Bornkamp
Maintainer: Bjoern Bornkamp <bbnkmp at gmail.com>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: yes
Citation: iterLap citation info
In views: Bayesian
CRAN checks: iterLap results

Downloads:

Reference manual: iterLap.pdf
Package source: iterLap_1.1-2.tar.gz
OS X binary: iterLap_1.1-2.tgz
Windows binary: iterLap_1.1-2.zip
Old sources: iterLap archive