Bernoulli
- Bernoulli
- Volume 23, Number 4B (2017), 3537-3570.
Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models
Prithwish Bhaumik and Subhashis Ghosal
Abstract
Often the regression function is specified by a system of ordinary differential equations (ODEs) involving some unknown parameters. Typically analytical solution of the ODEs is not available, and hence likelihood evaluation at many parameter values by numerical solution of equations may be computationally prohibitive. Bhaumik and Ghosal (Electron. J. Stat. 9 (2015) 3124–3154) considered a Bayesian two-step approach by embedding the model in a larger nonparametric regression model, where a prior is put through a random series based on B-spline basis functions. A posterior on the parameter is induced from the regression function by minimizing an integrated weighted squared distance between the derivative of the regression function and the derivative suggested by the ODEs. Although this approach is computationally fast, the Bayes estimator is not asymptotically efficient. In this paper, we suggest a modification of the two-step method by directly considering the distance between the function in the nonparametric model and that obtained from a four stage Runge–Kutta (RK$4$) method. We also study the asymptotic behavior of the posterior distribution of $\mathbf{\theta}$ based on an approximate likelihood obtained from an RK$4$ numerical solution of the ODEs. We establish a Bernstein–von Mises theorem for both methods which assures that Bayesian uncertainty quantification matches with the frequentist one and the Bayes estimator is asymptotically efficient.
Article information
Source
Bernoulli, Volume 23, Number 4B (2017), 3537-3570.
Dates
Received: November 2014
Revised: April 2016
First available in Project Euclid: 23 May 2017
Permanent link to this document
https://projecteuclid.org/euclid.bj/1495505101
Digital Object Identifier
doi:10.3150/16-BEJ856
Mathematical Reviews number (MathSciNet)
MR3654815
Zentralblatt MATH identifier
06778295
Keywords
approximate likelihood Bayesian inference Bernstein–von Mises theorem ordinary differential equation Runge–Kutta method spline smoothing
Citation
Bhaumik, Prithwish; Ghosal, Subhashis. Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models. Bernoulli 23 (2017), no. 4B, 3537--3570. doi:10.3150/16-BEJ856. https://projecteuclid.org/euclid.bj/1495505101
