Recursive nonlinear estimation. A geometric approach
type: Item
instance of: scholarly article
title: Recursive nonlinear estimation. A geometric approach
zbMATH Open document ID: 0860.62002
author: Rudolf Kulhavý
published in: Lecture Notes in Control and Information Sciences
publication date: 1996-08-08T00:00:00Z
review text: The author shows that statistical estimation procedures may be considered as distance minimizing. This is called a geometric view of estimation. A unifying approach is given involving maximum likelihood estimation, Kullback-Leibler distance, Shannon information measure and Bayesian estimation. A comprehensive discussion of the subject, many interconnections of related literature and a lot of evaluated numerical examples are presented. Although the mathematician may miss a more rigorous treatment of some subjects, the book may be of interest for both theoretically and practically oriented people.
Mathematics Subject Classification ID: 62-02
Mathematics Subject Classification ID: 62F10
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 62B10
zbMATH DE Number: 914530
zbMATH Keywords: nonlinear estimation
zbMATH Keywords: geometric view of estimation
zbMATH Keywords: unifying approach
zbMATH Keywords: maximum likelihood estimation
zbMATH Keywords: Kullback-Leibler distance
zbMATH Keywords: Shannon information measure
zbMATH Keywords: Bayesian estimation
reviewed by: Walter Schlee
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1007/bfb0031830
OpenAlex ID: W4244947583
DOI: 10.1007/BFB0031830
description: scientific article; zbMATH DE number 914530