AdMit

Authors

• David Ardia, University of Fribourg, Switzerland
• Lennart F. Hoogerheide, Erasmus University Rotterdam
• Herman K. van Dijk, Erasmus University Rotterdam

Date of this Version

6-2009

Document Type

Article

Citation

The R Journal (June 2009) 1(1)

Comments

Copyright 2009, The R Foundation. Open access material. License: CC BY 3.0 Unported

Abstract

This note presents the package AdMit (Ardia et al., 2008, 2009), an R implementation of the adaptive mixture of Student-t distributions (AdMit) procedure developed by Hoogerheide (2006); see also Hoogerheide et al. (2007); Hoogerheide and van Dijk (2008). The AdMit strategy consists of the construction of a mixture of Student-t distributions which approximates a target distribution of interest. The fitting procedure relies only on a kernel of the tar get density, so that the normalizing constant is not required. In a second step, this approximation is used as an importance function in importance sampling or as a candidate density in the independence chain Metropolis-Hastings (M-H) algorithm to estimate characteristics of the target density. The estimation procedure is fully automatic and thus avoids the difficult task, especially for non-experts, of tuning a sampling algorithm. Typically, the target is a posterior distribution in a Bayesian analysis, where we indeed often only know a kernel of the posterior density.