Bayesian Optimization for Robust Identification of Ornstein-Uhlenbeck Model

• URL: http://arxiv.org/abs/2503.06381v1
• Date: Sun, 09 Mar 2025 01:38:21 GMT
• Title: Bayesian Optimization for Robust Identification of Ornstein-Uhlenbeck Model
• Authors: Jinwen Xu, Qin Lu, Yaakov Bar-Shalom,
• Abstract summary: This paper deals with the identification of the derivation Ornstein-Uhlenbeck (OU) process error model.<n>We put forth a sample-efficient global optimization approach based on the Bayesian optimization framework.
• Score: 4.0148499400442095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper deals with the identification of the stochastic Ornstein-Uhlenbeck (OU) process error model, which is characterized by an inverse time constant, and the unknown variances of the process and observation noises. Although the availability of the explicit expression of the log-likelihood function allows one to obtain the maximum likelihood estimator (MLE), this entails evaluating the nontrivial gradient and also often struggles with local optima. To address these limitations, we put forth a sample-efficient global optimization approach based on the Bayesian optimization (BO) framework, which relies on a Gaussian process (GP) surrogate model for the objective function that effectively balances exploration and exploitation to select the query points. Specifically, each evaluation of the objective is implemented efficiently through the Kalman filter (KF) recursion. Comprehensive experiments on various parameter settings and sampling intervals corroborate that BO-based estimator consistently outperforms MLE implemented by the steady-state KF approximation and the expectation-maximization algorithm (whose derivation is a side contribution) in terms of root mean-square error (RMSE) and statistical consistency, confirming the effectiveness and robustness of the BO for identification of the stochastic OU process. Notably, the RMSE values produced by the BO-based estimator are smaller than the classical Cram\'{e}r-Rao lower bound, especially for the inverse time constant, estimating which has been a long-standing challenge. This seemingly counterintuitive result can be explained by the data-driven prior for the learning parameters indirectly injected by BO through the GP prior over the objective function.

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