Stochastic Modeling

Joseph L. Awange (), Béla Paláncz (), Robert H. Lewis () and Lajos Völgyesi ()
Additional contact information
Joseph L. Awange: Curtin University, Department of Spatial Sciences, School of Earth and Planetary Sciences
Béla Paláncz: Budapest University of Technology and Economics, Department of Geodesy and Surveying, Faculty of Civil Engineering
Robert H. Lewis: Fordham University
Lajos Völgyesi: Budapest University of Technology and Economics, Department of Geodesy and Surveying, Faculty of Civil Engineering

Chapter 15 in Mathematical Geosciences, 2023, pp 627-677 from Springer

Abstract: Abstract Comparison of modeling via stochastic differential equation system with parameter estimation to the Nearest Neighbors method as ML technique is illustrated. Machine Learning Differential Equation model is also introduced, employing deep neural network and stochastic differential equations in Ito-form. These methods are applied to image classification including Black-Hole optimization as well as to stochastic regression of time series.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-92495-9_15

Ordering information: This item can be ordered from
http://www.springer.com/9783030924959

DOI: 10.1007/978-3-030-92495-9_15

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().