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Cet ouvrage propose une introduction à la théorie des probabilités et à ses applications. Une première partie présente de manière complète et rigoureuse les probabilités discrètes. Une analyse détaillée de modèles issus de divers domaines est proposée (mathématique, informatique, physique, biologie). La seconde partie traite des variables aléatoires continues, réelles et multivariées, ainsi que des théorèmes limites classiques des probabilités, loi des grands nombres et théorème central limite. Parmi les applications, un chapitre est consacré à l’estimation de paramètres en statistique mathématique, un autre est dédié aux outils de simulation informatique de variables aléatoires. De nombreuses propositions de simulation émaillent et illustrent les différents modèles aléatoires présentés tout au long de l’ouvrage. Chaque chapitre propose une large sélection d’exercices de niveaux progressifs.
Now in its second edition, this textbook serves as an introduction to probability and statistics for non-mathematics majors who do not need the exhaustive detail and mathematical depth provided in more comprehensive treatments of the subject. The presentation covers the mathematical laws of random phenomena, including discrete and continuous random variables, expectation and variance, and common probability distributions such as the binomial, Poisson, and normal distributions. More classical examples such as Montmort's problem, the ballot problem, and Bertrand’s paradox are now included, along with applications such as the Maxwell-Boltzmann and Bose-Einstein distributions in physics. Key features in new edition: * 35 new exercises * Expanded section on the algebra of sets * Expanded chapters on probabilities to include more classical examples * New section on regression * Online instructors' manual containing solutions to all exercises“/p> Advanced undergraduate and graduate students in computer science, engineering, and other natural and social sciences with only a basic background in calculus will benefit from this introductory text balancing theory with applications. Review of the first edition: This textbook is a classical and well-written introduction to probability theory and statistics. ... the book is written ‘for an audience such as computer science students, whose mathematical background is not very strong and who do not need the detail and mathematical depth of similar books written for mathematics or statistics majors.’ ... Each new concept is clearly explained and is followed by many detailed examples. ... numerous examples of calculations are given and proofs are well-detailed." (Sophie Lemaire, Mathematical Reviews, Issue 2008 m)
This textbook is based on 20 years of teaching a graduate-level course in random processes to a constituency extending beyond signal processing, communications, control, and networking, and including in particular circuits, RF and optics graduate students. In order to accommodate today’s circuits students’ needs to understand noise modeling, while covering classical material on Brownian motion, Poisson processes, and power spectral densities, the author has inserted discussions of thermal noise, shot noise, quantization noise and oscillator phase noise. At the same time, techniques used to analyze modulated communications and radar signals, such as the baseband representation of bandpass random signals, or the computation of power spectral densities of a wide variety of modulated signals, are presented. This book also emphasizes modeling skills, primarily through the inclusion of long problems at the end of each chapter, where starting from a description of the operation of a system, a model is constructed and then analyzed. Provides semester-length coverage of random processes, applicable to the analysis of electrical and computer engineering systems; Designed to be accessible to students with varying backgrounds in undergraduate mathematics and engineering; Includes solved examples throughout the discussion, as well as extensive problem sets at the end of every chapter; Develops and reinforces student’s modeling skills, with inclusion of modeling problems in every chapter; Solutions for instructors included.
This well-respected text is designed for the first course in probability and statistics taken by students majoring in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical methods presented at a level that is accessible to students with only a calculus background. They explore the practical implications of the formal results to problem-solving so students gain an understanding of the logic behind the techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically for students in engineering and computer science and include opportunities for real data analysis.
A Complete Introduction to probability AND its computer Science Applications USING R Probability with R serves as a comprehensive and introductory book on probability with an emphasis on computing-related applications. Real examples show how probability can be used in practical situations, and the freely available and downloadable statistical programming language R illustrates and clarifies the book's main principles. Promoting a simulation- and experimentation-driven methodology, this book highlights the relationship between probability and computing in five distinctive parts: The R Language presents the essentials of the R language, including key procedures for summarizing and building graphical displays of statistical data. Fundamentals of Probability provides the foundations of the basic concepts of probability and moves into applications in computing. Topical coverage includes conditional probability, Bayes' theorem, system reliability, and the development of the main laws and properties of probability. Discrete Distributions addresses discrete random variables and their density and distribution functions as well as the properties of expectation. The geometric, binomial, hypergeometric, and Poisson distributions are also discussed and used to develop sampling inspection schemes. Continuous Distributions introduces continuous variables by examining the waiting time between Poisson occurrences. The exponential distribution and its applications to reliability are investigated, and the Markov property is illustrated via simulation in R. The normal distribution is examined and applied to statistical process control. Tailing Off delves into the use of Markov and Chebyshev inequalities as tools for estimating tail probabilities with limited information on the random variable. Numerous exercises and projects are provided in each chapter, many of which require the use of R to perform routine calculations and conduct experiments with simulated data. The author directs readers to the appropriate Web-based resources for installing the R software package and also supplies the essential commands for working in the R workspace. A related Web site features an active appendix as well as a forum for readers to share findings, thoughts, and ideas. With its accessible and hands-on approach, Probability with R is an ideal book for a first course in probability at the upper-undergraduate and graduate levels for readers with a background in computer science, engineering, and the general sciences. It also serves as a valuable reference for computing professionals who would like to further understand the relevance of probability in their areas of practice.
Cet ouvrage constitue une première introduction à la théorie des probabilités. A la fois rigoureux et didactique, il présente l'ensemble des notions et outils de base, et de manière approfondie, les deux théorèmes fondamentaux que sont la loi des grands nombres et le théorème de la limite centrale. Certains sujets, comme celui de l'espérance d'une variable aléatoire, sont traités plus en détail qu'usuellement dans un texte d'introduction. La théorie ainsi développée est appliquée d'une part à l'étude des chaînes de Markov, marches aléatoires et au modèle d'Ising, et d'autre part à des sujets classiques de statistique mathématique, estimations, tests, populations normalement distribuées. Les résultats sont démontrés dans leur intégralité, et de nombreux exemples jalonnent le texte. Cette référence s'adresse principalement aux étudiants de physique ou de mathématiques des universités et grandes écoles, maîtrisant au préalable les bases du calcul différentiel et intégral.
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.
This well-respected text is designed for the first course in probability and statistics taken by students majoring in Engineering and the Computing Sciences. The prerequisite is one year of calculus. The text offers a balanced presentation of applications and theory. The authors take care to develop the theoretical foundations for the statistical methods presented at a level that is accessible to students with only a calculus background. They explore the practical implications of the formal results to problem-solving so students gain an understanding of the logic behind the techniques as well as practice in using them. The examples, exercises, and applications were chosen specifically for students in engineering and computer science and include opportunities for real data analysis.