Noise Benefits in Expectation-Maximization Algorithms

24 Nov 2014 Osonde Adekorede Osoba

This dissertation shows that careful injection of noise into sample data can substantially speed up Expectation-Maximization algorithms. Expectation-Maximization algorithms are a class of iterative algorithms for extracting maximum likelihood estimates from corrupted or incomplete data... (read more)

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