From Statistical Physics to Statistical Inference and Back

J. P. Nadal

ISBN 10: 0792327756 / ISBN 13: 9780792327752

Published by Springer Netherlands, Springer Netherlands, 1994

About this Item

Druck auf Anfrage Neuware - Printed after ordering - Physicists, when modelling physical systems with a large number of degrees of freedom, and statisticians, when performing data analysis, have developed their own concepts and methods for making the `best' inference. But are these methods equivalent, or not What is the state of the art in making inferences The physicists want answers. More: neural computation demands a clearer understanding of how neural systems make inferences; the theory of chaotic nonlinear systems as applied to time series analysis could profit from the experience already booked by the statisticians; and finally, there is a long-standing conjecture that some of the puzzles of quantum mechanics are due to our incomplete understanding of how we make inferences. Matter enough to stimulate the writing of such a book as the present one. But other considerations also arise, such as the maximum entropy method and Bayesian inference, information theory and the minimum description length. Finally, it is pointed out that an understanding of human inference may require input from psychologists. This lively debate, which is of acute current interest, is well summarized in the present work.

Seller Inventory # 9780792327752

Bibliographic Details

• Title: From Statistical Physics to Statistical Inference and Back
• Publisher: Springer Netherlands, Springer Netherlands
• Publication Date: 1994
• Language: English
• ISBN 10: 0792327756
• ISBN 13: 9780792327752
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 25D millimeters 241 millimeters width by 160 millimeters height by 25 millimeters depth
• Weight: 717 grams

About this title

Synopsis

Physicists, when modelling physical systems with a large number of degrees of freedom, and statisticians, when performing data analysis, have developed their own concepts and methods for making the `best' inference. But are these methods equivalent, or not? What is the state of the art in making inferences? The physicists want answers. More: neural computation demands a clearer understanding of how neural systems make inferences; the theory of chaotic nonlinear systems as applied to time series analysis could profit from the experience already booked by the statisticians; and finally, there is a long-standing conjecture that some of the puzzles of quantum mechanics are due to our incomplete understanding of how we make inferences. Matter enough to stimulate the writing of such a book as the present one.
But other considerations also arise, such as the maximum entropy method and Bayesian inference, information theory and the minimum description length. Finally, it is pointed out that an understanding of human inference may require input from psychologists. This lively debate, which is of acute current interest, is well summarized in the present work.