Free shipping on all orders over $79* Limited Time Only
Track My Order
Help Centre
Likes
My Wish ListsLogin / Join
Booktopia
Australia's local bookstore
Get Free Shipping on orders over $79
Maximum Entropy, Information Without Probability and Complex Fractals : Classical and Quantum Approach - Guy Jumarie

Maximum Entropy, Information Without Probability and Complex Fractals

Classical and Quantum Approach

By: Guy Jumarie

eText | 17 April 2013

At a Glance

eText

$159.01

or 4 interest-free payments of $39.75 with

 or 

Instant online reading in your Booktopia eTextbook Library *

Why choose an eTextbook?

Instant Access *

Purchase and read your book immediately

Read Aloud

Listen and follow along as Bookshelf reads to you

Study Tools

Built-in study tools like highlights and more

* eTextbooks are not downloadable to your eReader or an app and can be accessed via web browsers only. You must be connected to the internet and have no technical issues with your device or browser that could prevent the eTextbook from operating.
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.
on
Desktop
Tablet
Mobile

You Can Find This eBook In

Non-FictionComputing & I.T.Computer ScienceMathematicsProbability & StatisticsSciencePhysicsApplied MathematicsReference, Information & Interdisciplinary SubjectsResearch & InformationInformation theoryCybernetics & Systems Theory

Other Editions and Formats

Paperback

Published: 9th December 2010

View Product

More in Computer Science

Amazon.com : Get Big Fast - Robert Spector

eBOOK

Amazon.com

Get Big Fast

eBook

$22.99

This is For Everyone - Tim Berners-Lee

eBOOK

This is For Everyone

eBook

$9.99

ReFormat : Windows 11 - Adam Natad

eBOOK

ReFormat

Windows 11

eBook

$8.43

This product is categorised by