Estimating parameters in stochastic systems: A variational Bayesian approach

DOI10.1016/j.physd.2011.08.013zbMath1229.62109OpenAlexW1998541716MaRDI QIDQ654174

Manfred Opper, Michail D. Vrettas, Dan Cornford

Publication date: 28 December 2011

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://publications.aston.ac.uk/id/eprint/16146/1/vrettasm_PhysD.pdf

zbMATH Keywords

dynamical systemsstochastic differential equationsBayesian inference

Mathematics Subject Classification ID

Related Items (5)

Ensemble MCMC: accelerating pseudo-marginal MCMC for state space models using the ensemble Kalman filter ⋮ Expectation propagation for continuous time stochastic processes ⋮ The stochastic quasi-chemical model for bacterial growth: variational Bayesian parameter update ⋮ Estimation algorithm for system with non‐Gaussian multiplicative/additive noises based on variational Bayesian inference ⋮ Generative Ensemble Regression: Learning Particle Dynamics from Observations of Ensembles with Physics-informed Deep Generative Models

Uses Software

• EnKF

Cites Work

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