[PDF] Full Chaines De Markov Theorie Algorithmes Et Applications Download eBook

Chaînes de Markov : Théorie, algorithmes et applications

Author: SERICOLA Bruno

Publisher: Lavoisier

Published: 2013-05-01

Total Pages: 391

ISBN-13: 2746289164

Les chaînes de Markov sont des modèles probabilistes utilisés dans des domaines variés comme la logistique, l'informatique, la fiabilité, les télécommunications, ou encore la biologie et la physique-chimie. On les retrouve également dans la finance, l’économie et les sciences sociales. Cet ouvrage présente une étude approfondie des chaînes de Markov à temps discret et à temps continu avec des applications détaillées aux processus de naissance et mort et aux files d'attente. Ces applications sont illustrées par des algorithmes généraux de calcul de probabilités d'état et de distribution de temps de passage. Le développement de ces algorithmes repose sur l'utilisation de la technique d'uniformisation des chaînes de Markov qui est présentée de manière théorique et intuitive. Ce livre s'adresse aux ingénieurs et chercheurs ayant besoin de modèles probabilistes pour évaluer et prédire le comportement des systèmes qu'ils étudient ou qu'ils développent. Il est aussi très bien adapté pour un cours de master.

Advances In Theory And Applications Of Random Sets: Proceedings Of The Symposium

Author: Dominique Jeulin

Publisher: World Scientific

Published: 1997-01-16

Total Pages: 338

ISBN-13: 9814546658

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This volume covers topics ranging from pure and applied mathematics to pedagogical issues in mathematics. There are papers in mathematical biology, differential equations, difference equations, dynamical systems, orthogonal polynomials, topology, calculus reform, algebra, and numerical analysis. Most of the papers include new, interesting results that are at the cutting edge of the respective subjects. However, there are some papers of an expository nature.

Mathematics for Informatics and Computer Science

Author: Pierre Audibert

Publisher: John Wiley & Sons

Published: 2013-03-01

Total Pages: 672

ISBN-13: 1118586506

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How many ways do exist to mix different ingredients, how many chances to win a gambling game, how many possible paths going from one place to another in a network ? To this kind of questions Mathematics applied to computer gives a stimulating and exhaustive answer. This text, presented in three parts (Combinatorics, Probability, Graphs) addresses all those who wish to acquire basic or advanced knowledge in combinatorial theories. It is actually also used as a textbook. Basic and advanced theoretical elements are presented through simple applications like the Sudoku game, search engine algorithm and other easy to grasp applications. Through the progression from simple to complex, the teacher acquires knowledge of the state of the art of combinatorial theory. The non conventional simultaneous presentation of algorithms, programs and theory permits a powerful mixture of theory and practice. All in all, the originality of this approach gives a refreshing view on combinatorial theory.

Seminaire de Probabilites XXXIII

Author: J. Azema

Publisher: Springer

Published: 2006-11-14

Total Pages: 432

ISBN-13: 3540484078

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Besides topics traditionally found in the Sminaire de Probabilits (Martingale Theory, Stochastic Processes, questions of general interest in Probability Theory), this volume XXXIII presents nine contributions to the study of filtrations up to isomorphism. It also contains three graduate courses: Dynamics of stochastic algorithms, by M. Benaim; Simulated annealing algorithms and Markov chains with rare transitions, by O. Catoni; and Concentration of measure and logarithmic Sobolev inequalities, by M. Ledoux. These up to date courses present the state of the art in three matters of interest to students in theoretical or applied Probability Theory, and to researchers as well.

Advances in Production Management Systems: New Challenges, New Approaches

Author: Bruno Vallespir

Publisher: Springer

Published: 2010-10-19

Total Pages: 681

ISBN-13: 3642163580

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The present economic and social environment has given rise to new situations within which companies must operate. As a first example, the globalization of the economy and the need for performance has led companies to outsource and then to operate inside networks of enterprises such as supply chains or virtual enterprises. A second instance is related to environmental issues. The statement about the impact of ind- trial activities on the environment has led companies to revise processes, to save - ergy, to optimize transportation.... A last example relates to knowledge. Knowledge is considered today to be one of the main assets of a company. How to capitalize, to manage, to reuse it for the benefit of the company is an important current issue. The three examples above have no direct links. However, each of them constitutes a challenge that companies have to face today. This book brings together the opinions of several leading researchers from all around the world. Together they try to develop new approaches and find answers to those challenges. Through the individual ch- ters of this book, the authors present their understanding of the different challenges, the concepts on which they are working, the approaches they are developing and the tools they propose. The book is composed of six parts; each one focuses on a specific theme and is subdivided into subtopics.

Hidden Markov Models for Bioinformatics

Author: T. Koski

Publisher: Springer Science & Business Media

Published: 2001-11-30

Total Pages: 422

ISBN-13: 9781402001352

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The purpose of this book is to give a thorough and systematic introduction to probabilistic modeling in bioinformatics. The book contains a mathematically strict and extensive presentation of the kind of probabilistic models that have turned out to be useful in genome analysis. Questions of parametric inference, selection between model families, and various architectures are treated. Several examples are given of known architectures (e.g., profile HMM) used in genome analysis.

Statistical Learning Theory and Stochastic Optimization

Author: Olivier Catoni

Publisher: Springer

Published: 2004-08-30

Total Pages: 278

ISBN-13: 3540445072

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Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in practice a notoriously "wrong'' (i.e. over-simplified) model to predict, estimate or classify. This point of view takes its roots in three fields: information theory, statistical mechanics, and PAC-Bayesian theorems. Results on the large deviations of trajectories of Markov chains with rare transitions are also included. They are meant to provide a better understanding of stochastic optimization algorithms of common use in computing estimators. The author focuses on non-asymptotic bounds of the statistical risk, allowing one to choose adaptively between rich and structured families of models and corresponding estimators. Two mathematical objects pervade the book: entropy and Gibbs measures. The goal is to show how to turn them into versatile and efficient technical tools, that will stimulate further studies and results.