Variational State and Parameter Estimation

• URL: http://arxiv.org/abs/2012.07269v1
• Date: Mon, 14 Dec 2020 05:35:29 GMT
• Title: Variational State and Parameter Estimation
• Authors: Jarrad Courts and Johannes Hendriks and Adrian Wills and Thomas Sch\"on and Brett Ninness
• Abstract summary: This paper considers the problem of computing Bayesian estimates of both states and model parameters for nonlinear state-space models. A variational approach is used to provide an assumed density which approximates the desired, intractable, distribution. The proposed method is compared against state-of-the-art Hamiltonian Monte Carlo in two numerical examples.
• Score: 0.8049701904919515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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