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1. |
Record Nr. |
UNINA9910301335603321 |
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Titolo |
Documents diplomatiques (Annuel) |
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Pubbl/distr/stampa |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Periodico |
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2. |
Record Nr. |
UNINA9910300123303321 |
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Autore |
Collet Jean-François |
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Titolo |
Discrete Stochastic Processes and Applications / / by Jean-François Collet |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
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ISBN |
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Edizione |
[1st ed. 2018.] |
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Descrizione fisica |
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1 online resource (XVII, 220 p. 3 illus.) |
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Collana |
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Universitext, , 0172-5939 |
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Disciplina |
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Soggetti |
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Probabilities |
Probability Theory and Stochastic Processes |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Preface -- I. Markov processes -- 1. Discrete time, countable space -- 2. Linear algebra and search engines -- 3. The Poisson process -- 4. Continuous time, discrete space -- 5. Examples -- II. Entropy and applications -- 6. Prelude: a user's guide to convexity -- 7. The basic quantities of information theory -- 8. An example of application: binary coding -- A. Some useful facts from calculus -- B. Some useful facts from probability -- C. Some useful facts from linear algebra -- D. An arithmetical lemma -- E. Table of exponential families -- References -- Index. |
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Sommario/riassunto |
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This unique text for beginning graduate students gives a self-contained introduction to the mathematical properties of stochastics |
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and presents their applications to Markov processes, coding theory, population dynamics, and search engine design. The book is ideal for a newly designed course in an introduction to probability and information theory. Prerequisites include working knowledge of linear algebra, calculus, and probability theory. The first part of the text focuses on the rigorous theory of Markov processes on countable spaces (Markov chains) and provides the basis to developing solid probabilistic intuition without the need for a course in measure theory. The approach taken is gradual beginning with the case of discrete time and moving on to that of continuous time. The second part of this text is more applied; its core introduces various uses of convexity in probability and presents a nice treatment of entropy. |
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