Adaptive Mixture of Student-t Distributions

Provides functions to perform the fitting of an adaptive mixture of Student-t distributions to a target density through its kernel function as described in Ardia et al. (2009) . The mixture approximation can then be used as the importance density in importance sampling or as the candidate density in the Metropolis-Hastings algorithm to obtain quantities of interest for the target density itself.

AdMit

AdMit (Ardia et al., 2009a) is an R package which provides flexible functions to approximate a certain target distribution and to efficiently generate a sample of random draws from it, given only a kernel of the target density function. The core algorithm fits an adaptive mixture of Student-t distributions to the density of interest, and then, importance sampling or the independence chain Metropolis-Hastings algorithm is used to obtain quantities of interest for the target density, using the fitted mixture as the importance or candidate density. The estimation procedure is fully automatic and thus avoids the time-consuming and difficult task of tuning a sampling algorithm. Full description of the algorithm and numerous applications are available in Ardia et al. (2009a) and Ardia et al. (2009b).

Please cite the package in publications!

By using AdMit you agree to the following rules:

1. You must cite Ardia et al. (2009a) in working papers and published papers that use AdMit.
2. You must place the following URL in a footnote to help others find AdMit: https://CRAN.R-project.org/package=AdMit.
3. You assume all risk for the use of AdMit.

Ardia, D., Hoogerheide, L., van Dijk, H.K. (2009a).
Adaptive mixture of Student-t distributions as a flexible candidate distribution for efficient simulation: The R package AdMit.
Journal of Statistical Software, 29(3), 1-32.
https://doi.org/10.18637/jss.v029.i03

Ardia, D., Hoogerheide, L., van Dijk, H.K. (2009b).
AdMit: Adaptive mixture of Student-t distributions.
R Journal, 1(1), 25-30.
https://doi.org/10.32614/RJ-2009-003