Accelerating inference for diffusions observed with measurement error and large sample sizes using approximate Bayesian computation

DOI10.1080/00949655.2014.1002101OpenAlexW3101747691WikidataQ61858143 ScholiaQ61858143MaRDI QIDQ5222326

Julie Lyng Forman, Umberto Picchini

Publication date: 1 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1310.0973

zbMATH Keywords

stochastic differential equationprotein foldingMCMClikelihood-free inference

Mathematics Subject Classification ID

Non-Markovian processes: estimation (62M09) Bayesian inference (62F15) Biochemistry, molecular biology (92C40) Diffusion processes (60J60)

Related Items (6)

Delayed Acceptance ABC-SMC ⋮ Hierarchical nonlinear spatio-temporal agent-based models for collective animal movement ⋮ Coupling stochastic EM and approximate Bayesian computation for parameter inference in state-space models ⋮ Bayesian model comparison with un-normalised likelihoods ⋮ Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs ⋮ Multifidelity Approximate Bayesian Computation with Sequential Monte Carlo Parameter Sampling

Uses Software

• SDE Toolbox
• Julia
• LibBi
• abc-sde

Cites Work

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• Particle Markov Chain Monte Carlo Methods
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• Expectation Propagation for Likelihood-Free Inference
• Inference for Stereological Extremes
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• An adaptive Metropolis algorithm

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