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Variational State and Parameter Estimation⁎This research was financially supported by the project Learning fexible models for nonlinear dynamics (contract number: 2017-03807), funded by the Swedish Research Council and by Kjell och Märta Beijer Foundation and by the project AI4Research at Uppsala University. Issue 7 (2021)
Record Type:
Journal Article
Title:
Variational State and Parameter Estimation⁎This research was financially supported by the project Learning fexible models for nonlinear dynamics (contract number: 2017-03807), funded by the Swedish Research Council and by Kjell och Märta Beijer Foundation and by the project AI4Research at Uppsala University. Issue 7 (2021)
Main Title:
Variational State and Parameter Estimation⁎This research was financially supported by the project Learning fexible models for nonlinear dynamics (contract number: 2017-03807), funded by the Swedish Research Council and by Kjell och Märta Beijer Foundation and by the project AI4Research at Uppsala University.
Abstract: This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. Generally, this problem does not have a tractable solution and approximations must be utilised. In this work, a variational approach is used to provide an assumed density which approximates the desired, intractable, distribution. The approach is deterministic and results in an optimisation problem of a standard form. Due to the parametrisation of the assumed density selected first and second order derivatives are readily available which allows for efficient solutions. The proposed method is compared against state-of-the-art Hamiltonian Monte Carlo in two numerical examples.