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.
- Version1.1-4
- R versionunknown
- LicenseGPL-2
- LicenseGPL-3
- Needs compilation?Yes
- iterLap citation info
- Last release09/30/2023
Documentation
Team
Bjoern Bornkamp
MaintainerShow author details
Insights
Last 30 days
This package has been downloaded 194 times in the last 30 days. Enough downloads to make a small wave in the niche community. The curiosity is spreading! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 7 times.
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Last 365 days
This package has been downloaded 3,410 times in the last 365 days. Now we’re talking! This work is officially 'heard of in academic circles', just like those wild research papers on synthetic bananas. The day with the most downloads was Sep 11, 2024 with 42 downloads.
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Dependencies
- Depends2 packages