Maximum Entropy and Bayesian Methods in Applied Statistics

James H. Justice

ISBN 10: 0521096030 / ISBN 13: 9780521096034

Published by Cambridge University Press, 2009

About this Item

Druck auf Anfrage Neuware - Printed after ordering - This collection of papers contains contributions showing the use of the maximum entropy method in many of the fields in which it finds application. Seller Inventory # 9780521096034

Bibliographic Details

Title: Maximum Entropy and Bayesian Methods in ...

Publisher: Cambridge University Press

Publication Date: 2009

Binding: Taschenbuch

Condition: Neu

About this title

Synopsis:

This collection of papers by leading researchers in their respective fields contains contributions showing the use of the maximum entropy method in many of the fields in which it finds application. In the physical, mathematical and biological sciences it is often necessary to make inferences based on insufficient data. The problem of choosing one among the many possible conclusions or models which are compatible with the data may be resolved in a variety of ways. A particularly appealing method is to choose the solution which maximizes entropy in the sense that the conclusion or model honours the observed data but implies no further assumptions not warranted by the data. The maximum entropy principle has been growing in importance and acceptance in many fields, perhaps most notably statistical physics, astronomy, geophysics, signal processing, image analysis and physical chemistry. The papers included in this volume touch on most of the current areas of research activity and application, and will be of interest to research workers in all fields in which the maximum entropy method may be applied.

Book Description:

This collection of papers by leading researchers in their respective fields contains contributions showing the use of the maximum entropy method in many of the fields in which it finds application. In the physical, mathematical and biological sciences it is often necessary to make inferences based on insufficient data.