Maximum Entropy, Information Without Probability and Complex Fractals: Classical and Quantum Approach

Jumarie, Guy

ISBN 10: 0792363302 / ISBN 13: 9780792363309

Published by Kluwer Boston Incorporated, 2000

About this Item

Presents material on three topics, namely the amount of information involved in non-random functions, the amount of information involved in non-probabilistic square matrices (which are not quantum density matrices), and a model of complex-valued fractional Brownian motion of order n defined via random walks in the complex plane. Series: Fundamental Theories of Physics. Num Pages: 289 pages, biography. BIC Classification: GPF; PBW. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 235 x 155 x 17. Weight in Grams: 1310. . 2000. 2000th Edition. hardcover. . . . . Books ship from the US and Ireland.

Seller Inventory # V9780792363309

Bibliographic Details

• Title: Maximum Entropy, Information Without Probability and Complex Fractals: Classical and Quantum Approach
• Publisher: Kluwer Boston Incorporated
• Publication Date: 2000
• Language: English
• ISBN 10: 0792363302
• ISBN 13: 9780792363309
• Binding: Hardcover
• Condition: New

About this title

Synopsis

Every thought is a throw of dice. Stephane Mallarme This book is the last one of a trilogy which reports a part of our research work over nearly thirty years (we discard our non-conventional results in automatic control theory and applications on the one hand, and fuzzy sets on the other), and its main key words are Information Theory, Entropy, Maximum Entropy Principle, Linguistics, Thermodynamics, Quantum Mechanics, Fractals, Fractional Brownian Motion, Stochastic Differential Equations of Order n, Stochastic Optimal Control, Computer Vision. Our obsession has been always the same: Shannon's information theory should play a basic role in the foundations of sciences, but subject to the condition that it be suitably generalized to allow us to deal with problems which are not necessarily related to communication engineering. With this objective in mind, two questions are of utmost importance: (i) How can we introduce meaning or significance of information in Shannon's information theory? (ii) How can we define and/or measure the amount of information involved in a form or a pattern without using a probabilistic scheme? It is obligatory to find suitable answers to these problems if we want to apply Shannon's theory to science with some chance of success. For instance, its use in biology has been very disappointing, for the very reason that the meaning of information is there of basic importance, and is not involved in this approach.