MCMC-driven importance samplers

DOI10.1016/j.apm.2022.06.027zbMath1505.62468arXiv2105.02579OpenAlexW3157699234MaRDI QIDQ2110113

Víctor Elvira, E. Curbelo, David Delgado, Luca Martino, Fernando Rodriguez Llorente

Publication date: 21 December 2022

Published in: Applied Mathematical Modelling (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2105.02579

zbMATH Keywords

importance samplingcomputational algorithmsquadrature methodsBayesian inference

Mathematics Subject Classification ID

Bayesian inference (62F15) Sampling theory, sample surveys (62D05)

Related Items (1)

Generalized integral transform and Hamiltonian Monte Carlo for Bayesian structural damage identification

Cites Work

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• Adaptive Rejection Sampling for Gibbs Sampling
• Marginal Likelihood Computation for Model Selection and Hypothesis Testing: An Extensive Review

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