2020/05/14 by Sharon Lee, Geoffrey J. McLachlan, Lee, Sharon X. +3
Computer Science · #Algorithms and Data Compression #Bayesian Methods and Mixture Models #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning and Algorithms
paper · pdf · doi:10.48550/arxiv.2005.06848
openalex publication_date 2020/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Finite mixture models are powerful tools for modelling and analyzing heterogeneous data. Parameter estimation is typically carried out using maximum likelihood estimation via the Expectation-Maximization (EM) algorithm. Recently, the adoption of flexible distributions as component densities has become increasingly popular. Often, the EM algorithm for these models involves complicated expressions that are time-consuming to evaluate numerically. In this paper, we describe a parallel implementation of the EM-algorithm suitable for both single-threaded and multi-threaded processors and for both single machine and multiple-node systems. Numerical experiments are performed to demonstrate the potential performance gain n different settings. Comparison is also made across two commonly used platforms - R and MATLAB. For illustration, a fairly general mixture model is used in the comparison.