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140122  eng 
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a 9789401594967

100 
1 

a Jumarie, Guy

245 
0 
0 
a Maximum Entropy, Information Without Probability and Complex Fractals
h Elektronische Ressource
b Classical and Quantum Approach
c by Guy Jumarie

250 


a 1st ed. 2000

260 


a Dordrecht
b Springer Netherlands
c 2000, 2000

300 


a XIX, 270 p
b online resource

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0 

a 1. Introduction  2. Summary of Information Theory  3. Path Entropies of Non Random Functions  4. Path Entropies of Random Functions and of NonRandom Distributed Functions  5. Quantum Entropies of NonProbabilistic Square Matrices  6. ComplexValued Fractional Brownian Motion of Order n. Part I  7. ComplexValued Fractional Brownian Motion of Order n. Part II  8. Information Thermodynamics and ComplexValued Fractional Brownian motion of Order n  9. Fractals, Path Entropy, and Fractional FokkerPlanck Equation  10. Outline of Applications

653 


a Coding and Information Theory

653 


a Complex Systems

653 


a Coding theory

653 


a Probability Theory

653 


a System theory

653 


a Information theory

653 


a Mathematical physics

653 


a Applications of Mathematics

653 


a Mathematics

653 


a Theoretical, Mathematical and Computational Physics

653 


a Probabilities

041 
0 
7 
a eng
2 ISO 6392

989 


b SBA
a Springer Book Archives 2004

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0 

a Fundamental Theories of Physics

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5 
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a 10.1007/9789401594967

856 
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0 
u https://doi.org/10.1007/9789401594967?nosfx=y
x Verlag
3 Volltext

082 
0 

a 003.54

520 


a 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 nonconventional 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
