Importance sampling type estimators based on approximate marginal Markov chain Monte Carlo

DOI10.1111/SJOS.12492zbMath1467.62032arXiv1609.02541OpenAlexW3083615170WikidataQ109549752 ScholiaQ109549752MaRDI QIDQ4994799

Jordan Franks, Matti Vihola

Publication date: 22 June 2021

Published in: Scandinavian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1609.02541

zbMATH Keywords

importance samplingMarkov chain Monte Carlosequential Monte Carlodelayed acceptanceunbiased estimatorpseudo-marginal method

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Monte Carlo methods (65C05) Sequential statistical analysis (62L10)

Related Items (4)

Unbiased parameter inference for a class of partially observed Lévy-process models ⋮ Efficiency of delayed-acceptance random walk metropolis algorithms ⋮ Unbiased Inference for Discretely Observed Hidden Markov Model Diffusions ⋮ On resampling schemes for particle filters with weakly informative observations

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