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This paper presents the R package AdMit which provides task of tuning a sam- pling algorithm. The relevance of the package is shown in two examples. The rst aims at illustrating in detail the use of the functions provided by the package in a bivariate bimodal distribution. The second shows the relevance of the adaptive mixture procedure through the Bayesian estimation of a mixture of ARCH model tted to foreign exchange log-returns data. The methodology is compared to standard cases of importance sampling and the Metropolis-Hastings algorithm using a naive candidate and with the Griddy-Gibbs approach.

journal of statistical software

Adaptive Mixture of Student-<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:math>Distributions as a Flexible Candidate Distribution for Efficient Simulation: The<i>R</i>Package<b>AdMit</b>

Adaptive Mixture of Student-tDistributions as a Flexible Candidate Distribution for Efficient Simulation: TheRPackageAdMit