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.
- Bjoern Bornkamp
- Date of publication
- 2012-05-22 20:52:08
- Bjoern Bornkamp <email@example.com>
- Gelman-Rubin mode approximation
- Monte Carlo sampling using the iterated Laplace...
- Iterated Laplace Approximation
- iterLap package internal functions
- iterLap package information
- Residual resampling
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