Surrogate-assisted parallel tempering for Bayesian neural learning

Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) methods typically implement Bayesian inference which faces several challenges given a large number of parameters, complex and...

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Veröffentlicht in:	arXiv.org 2020-05
Hauptverfasser:	Chandra, Rohitash, Jain, Konark, Kapoor, Arpit, Ashray Aman
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayesian analysis Computation Computer simulation Data management Decision making Machine learning Markov analysis Markov chains Mathematical models Monte Carlo simulation Optimization Parameters Tempering
Online-Zugang:	Volltext
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