A High Accuracy Stochastic Estimation of a Nonlinear Deterministic Model

TL;DR

This paper proposes a stochastic estimation method for nonlinear deterministic models using MCMC, achieving high accuracy by employing models with very small variances to closely approximate the deterministic system.

Contribution

It introduces a novel stochastic estimation approach with minimal variance that effectively approximates nonlinear deterministic models, leveraging MCMC techniques.

Findings

High accuracy in model estimation demonstrated

Stochastic approach closely matches deterministic models

Efficient estimation with small variance models

Abstract

In this paper, an approach to estimating a nonlinear deterministic model is presented. We introduce a stochastic model with extremely small variances so that the deterministic and stochastic models are essentially indistinguishable from each other. This point is explained in the paper. The estimation is then carried out using stochastic optimisation based on Markov chain Monte Carlo (MCMC) methods.