Estimation of parameters in mean-reverting stochastic systems
type: Item
instance of: scholarly article
title: Estimation of parameters in mean-reverting stochastic systems
zbMATH Open document ID: 1407.93376
DOI: 10.1155/2014/317059
author: Tianhai Tian
author: Yanli Zhou
author: Yonghong Wu
author: Xiangyu Ge
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: Stochastic differential equation (SDE) is a very important mathematical tool to describe complex systems in which noise plays an important role. SDE models have been widely used to study the dynamic properties of various nonlinear systems in biology, engineering, finance, and economics, as well as physical sciences. Since a SDE can generate unlimited numbers of trajectories, it is difficult to estimate model parameters based on experimental observations which may represent only one trajectory of the stochastic model. Although substantial research efforts have been made to develop effective methods, it is still a challenge to infer unknown parameters in SDE models from observations that may have large variations. Using an interest rate model as a test problem, in this work we use the Bayesian inference and Markov Chain Monte Carlo method to estimate unknown parameters in SDE models.
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 60H10
zbMATH DE Number: 7016157
Wikidata QID: Q59065831
describes a project that uses: BayesDA
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/317059
OpenAlex ID: W2005209233
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description: scientific article; zbMATH DE number 7016157