Properties of the stochastic approximation EM algorithm with mini-batch sampling

To deal with very large datasets a mini-batch version of the Monte Carlo Markov Chain Stochastic Approximation Expectation–Maximization algorithm for general latent variable models is proposed. For exponential models the algorithm is shown to be convergent under classical conditions as the number of...

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Veröffentlicht in:	Statistics and computing Jg. 30; H. 6; S. 1725 - 1739
Hauptverfasser:	Kuhn, Estelle, Matias, Catherine, Rebafka, Tabea
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.11.2020 Springer Nature B.V Springer Verlag (Germany)
Schlagworte:	Algorithms Approximation Artificial Intelligence Computing time Convergence Markov chains Mathematical analysis Mathematics and Statistics Methodology Probability and Statistics in Computer Science Sampling Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs Statistics Theory Stochastic approximation Mini-batch sampling 62F12 65C60 EM algorithm Monte Carlo Markov chain stochastic approximation mini-batch sampling Monte Carlo Markov chain Mathematics Subject Classification 65C60 · 62F12
ISSN:	0960-3174, 1573-1375
Online-Zugang:	Volltext
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