Efficient Bayes inference in neural networks through adaptive importance sampling

DOI10.1016/j.jfranklin.2023.08.044arXiv2210.00993MaRDI QIDQ6082841

Jean-Christophe Pesquet, Víctor Elvira, Yunshi Huang, Emilie Chouzenoux

Publication date: 30 October 2023

Published in: Journal of the Franklin Institute (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2210.00993

Mathematics Subject Classification ID

Artificial intelligence (68Txx)

Cites Work

• Langevin diffusions and Metropolis-Hastings algorithms
• Langevin incremental mixture importance sampling
• Convergence rates for optimised adaptive importance samplers
• Generalized multiple importance sampling
• Layered adaptive importance sampling
• Adaptive Multiple Importance Sampling
• An Adaptive Population Importance Sampler: Learning From Uncertainty
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Efficient Bayesian Computation by Proximal Markov Chain Monte Carlo: When Langevin Meets Moreau
• Majorize–Minimize Adapted Metropolis–Hastings Algorithm
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