Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes
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Publication:2675612
DOI10.1016/j.jcp.2022.111543OpenAlexW3208426334MaRDI QIDQ2675612
Thomas P. Prescott, Ruth E. Baker, David J. Warne, Matthew J. Simpson
Publication date: 24 September 2022
Published in: Journal of Computational Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2110.14082
Bayesian inferenceapproximate Bayesian computationbiochemical reaction networksmultilevel Monte Carlomultifidelity rejection samplingpartially observed Markov processes
Parametric inference (62Fxx) Stochastic analysis (60Hxx) Probabilistic methods, stochastic differential equations (65Cxx)
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MLMCandMultifidelityForABC ⋮ Efficient Bayesian inference with latent Hamiltonian neural networks in no-U-turn sampling ⋮ Efficient multifidelity likelihood-free Bayesian inference with adaptive computational resource allocation
Uses Software
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