About this book

Introduction

In a close analogy to matching data in Euclidean space, this monograph views parameter estimation as matching of the empirical distribution of data with a model-based distribution. Using an appealing Pythagorean-like geometry of the empirical and model distributions, the book brings a new solution to the problem of recursive estimation of non-Gaussian and nonlinear models which can be regarded as a specific approximation of Bayesian estimation. The cases of independent observations and controlled dynamic systems are considered in parallel; the former case giving initial insight into the latter case which is of primary interest to the control community. A number of examples illustrate the key concepts and tools used. This unique monograph follows some previous results on the Pythagorean theory of estimation in the literature (e.g., Chentsov, Csiszar and Amari) but extends the results to the case of controlled dynamic systems.

Keywords

control distribution geometry model optimization probability probability theory space

Bibliographic information

  • DOI https://doi.org/10.1007/BFb0031830
  • Copyright Information Springer-Verlag London Limited 1996
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-540-76063-4
  • Online ISBN 978-3-540-40947-2
  • Series Print ISSN 0170-8643
  • Series Online ISSN 1610-7411
  • About this book