Towards scaling up markov chain monte carlo , an adaptive subsampling approach

Towards scaling up markov chain monte carlo , an adaptive subsampling approach

Los mťodos Monte Carlo mediante cadenas de Markov se estiman a menudo como demasiado intensivos en třminos computacionales para tener algn͠ uso prc̀tico para grandes conjuntos de datos. En este documento se describe una metodologa̕ que pretende escalar el algoritmo Metropolis-Hastings (MH) en este...

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Detaylı Bibliyografya
Diğer Yazarlar: Bardenet Rm̌i, Doucet Arnaud, Holmes Chris, Proceedings of Machine Learning Research (PMLR)
Materyal Türü: Kitap
Dil:İngilizce
Konular:
Anl̀isis de contenido
Administracin̤
Investigacin̤ operacional
Subject analysis
Management
Operations research
Ciencias de la administracin̤
Anl̀isis de markov
cadenas de markov
Mťodos monte carlo mediante cadenas de markov
Algoritmo metropolis-hastings
Submuestreo adaptativo
Administration sciences
Markov analysis
Markov chains
Monte carlo methods using markov chains
Metropolis-hastings algorithm
Adaptive subsampling
Online Erişim:Towards scaling up markov chain monte carlo , an adaptive subsampling approach
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Towards scaling up markov chain monte carlo , an adaptive subsampling approach

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