Parameter estimation in SDEs via the Fokker-Planck equation: likelihood function and adjoint based gradient computation

DOI10.1016/J.JMAA.2018.05.048zbMath1390.60210OpenAlexW2804977737WikidataQ129815108 ScholiaQ129815108MaRDI QIDQ1650494

Barbara Pedretscher, Barbara Kaltenbacher

Publication date: 4 July 2018

Published in: Journal of Mathematical Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmaa.2018.05.048

zbMATH Keywords

stochastic differential equationparameter identificationlikelihood functionstate space modeladjoint method

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) PDEs with randomness, stochastic partial differential equations (35R60) Fokker-Planck equations (35Q84)

Related Items (3)

Continuous‐time stochastic gradient descent for optimizing over the stationary distribution of stochastic differential equations ⋮ On the Asymptotical Regularization for Linear Inverse Problems in Presence of White Noise ⋮ Parameter identification and uncertainty quantification in stochastic state space models and its application to texture analysis

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• A decision-making Fokker-Planck model in computational neuroscience
• Nonparametric estimation of scalar diffusions based on low frequency data
• On parameter identification in stochastic differential equations by penalized maximum likelihood
• Integration based profile likelihood calculation for PDE constrained parameter estimation problems

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