getGrid

getStartVal

iterLap.control

LSfitgeq0

nlm.control

samplingMixDist

summary.mixDist

print.mixDist

print.summary.mixDist

checkStop

calcDist

extractMoments

dmvnormAdaptedC

dmvtAdaptedC

nlopt

calcHess

<p>These functions are not meant to be called by the user.</p>

 internal

iterLap-internal

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.

Bjoern Bornkamp

iterLap

Approximate Probability Densities by Iterated Laplace
Approximations

iterLap-internal function

iterLap package internal functions — iterLap-internal

iterLap package internal functions

iterLap-internal: iterLap package internal functions

Description

Arguments