Approximate blocked Gibbs sampling for Bayesian neural networks

DOI10.1007/s11222-023-10285-5zbMath1523.62030arXiv2208.11389OpenAlexW4385741616MaRDI QIDQ6063143

Theodore Papamarkou

Publication date: 7 November 2023

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2208.11389

zbMATH Keywords

Bayesian inferenceposterior predictive distributionBayesian neural networksblocked Gibbs samplingapproximate MCMCminibatch sampling

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Monte Carlo methods (65C05)

Cites Work

• Bayesian methods for neural networks and related models
• Adaptive Gibbs samplers and related MCMC methods
• Challenges in Markov chain Monte Carlo for Bayesian neural networks
• Split-and-Augmented Gibbs Sampler—Application to Large-Scale Inference Problems
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• The elements of statistical learning. Data mining, inference, and prediction
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Approximate blocked Gibbs sampling for Bayesian neural networks