The iterLap (iterated Laplace approximation) algorithm approximates a general (possibly nonnormalized) 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 


Author  Bjoern Bornkamp 
Maintainer  Bjoern Bornkamp <[email protected]> 
License  GPL 
Version  1.13 
Package repository  View on CRAN 
Installation 
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