This paper presents the parallel computing implementation of the MitISEM algorithm, labeled Parallel MitISEM. The basic MitISEM algorithm 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.
Parallelization experience with four canonical econometric models using ParMitISEM
2016-01-01
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
ddc:330 , C11 , C13 , C32 , C36 , importance sampling , parallel computing , MitISEM , MCMC
Multilevel Parallelization Models in CFD
AIAA | 2004
|Spatial Econometric Models for Panel Data
Transportation Research Record | 2005
|Multilevel Parallelization Models for High-Order CFD
AIAA | 2004
|Freight and Passenger Modeling Using Hybrid Econometric-Network Models
Online Contents | 2010
|Freight and Passenger Modeling Using Hybrid Econometric–Network Models
Transportation Research Record | 2010
|