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.
Package details |
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Author | Bjoern Bornkamp |
Maintainer | Bjoern Bornkamp <bbnkmp@mail.de> |
License | GPL |
Version | 1.1-4 |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
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