Spectral density-based and measure-preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEs
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Publication:2302513
DOI10.1007/s11222-019-09909-6zbMath1505.62081arXiv1903.01138OpenAlexW2987732919WikidataQ90054048 ScholiaQ90054048MaRDI QIDQ2302513
Massimiliano Tamborrino, Irene Tubikanec, Evelyn Buckwar
Publication date: 26 February 2020
Published in: Statistics and Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1903.01138
stochastic differential equationsinvariant measureapproximate Bayesian computationlikelihood-free inferencenumerical splitting schemesneural mass models
Computational methods for problems pertaining to statistics (62-08) Bayesian inference (62F15) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Computational methods for stochastic equations (aspects of stochastic analysis) (60H35) Numerical solutions to stochastic differential and integral equations (65C30)
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