Hamiltonian Markov chain Monte Carlo for partitioned sample spaces with application to Bayesian deep neural nets

DOI10.1007/s42952-019-00001-3zbMath1485.62005OpenAlexW3038137185MaRDI QIDQ2131889

Jaeyong Lee, Minchul Kim

Publication date: 27 April 2022

Published in: Journal of the Korean Statistical Society (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s42952-019-00001-3

zbMATH Keywords

bridge samplingparallelizationimage classificationHamiltonian Monte CarloBayesian neural network

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Computational methods for problems pertaining to statistics (62-08) Artificial neural networks and deep learning (68T07) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40) Parallel numerical computation (65Y05)

Related Items (1)

Parallelizing MCMC sampling via space partitioning

Uses Software

• TensorFlow
• emcee
• Adam

Cites Work

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• Ensemble samplers with affine invariance
• A tutorial on bridge sampling
• Bayesian learning for neural networks
• Monte Carlo strategies in scientific computing.
• Analysis of Initial Transient Deletion for Parallel Steady-State Simulations
• A Stochastic Approximation Method
• Simulating normalizing constants: From importance sampling to bridge sampling to path sampling

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