# 16-005/III (2016-01-22)

Nalan Basturk, Maastricht University, the Netherlands; Stefano Grassi, University of Kent, United Kingdom; Lennart Hoogerheide, VU University Amsterdam, the Netherlands; Herman K. van Dijk, VU University Amsterdam, Erasmus University Rotterdam, the Netherlands
finite mixtures, Student-t distributions, Importance Sampling, MCMC, Metropolis-Hastings algorithm, Expectation Maximization, Bayesian inference
JEL codes:
C11, C13, C23, C32

This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm, introduced by Hoogerheide, Opschoor and Van Dijk (2012), provides an automatic and flexible method to approximate a non-elliptical target density using adaptive mixtures of Student-t densities, where only a kernel of the target density is required. The approximation can be used as a candidate density in Importance Sampling or Metropolis Hastings methods for Bayesian inference on model parameters and probabilities. We present and discuss four canonical econometric models using a Graphics Processing Unit and a multi-core Central Processing Unit version of the MitISEM algorithm. The results show that the parallelization of the MitISEM algorithm on Graphics Processing Units and multi-core Central Processing Units is straightforward and fast to program using MATLAB. Moreover the speed performance of the Graphics Processing Unit version is much higher than the Central Processing Unit one.