Bayesian Inference Based on Stationary Fokker-Planck Sampling

DOI10.1162/NECO.2010.01-09-943zbMath1215.68196arXiv0901.1144OpenAlexW2020938661WikidataQ51613849 ScholiaQ51613849MaRDI QIDQ3568379

Arturo Berrones

Publication date: 11 June 2010

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/0901.1144

zbMATH Keywords

Bayesian learningcomplex inference models

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Cites Work

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• Langevin diffusions and Metropolis-Hastings algorithms
• Markov chain Monte Carlo methods for stochastic volatility models.
• Variational Bayes Solution of Linear Neural Networks and Its Generalization Performance
• Nonlinear Time Series Analysis
• Hyperparameter estimation for satellite image restoration using a MCMC maximum-likelihood method

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