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• W3025127730 abstract "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." @default.
• W3025127730 created "2020-05-21" @default.
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• W3025127730 date "2020-05-14" @default.
• W3025127730 modified "2023-09-27" @default.
• W3025127730 title "Multi-Node EM Algorithm for Finite Mixture Models" @default.
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• W3025127730 hasPublicationYear "2020" @default.
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