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À partir de connaissances élémentaires sur les probabilités, cet ouvrage propose un cours approfondi de physique mathématique stochastique. D’une part, il expose l’aspect classique des applications probabilistes aux sciences physiques, et introduit les principales notions dans un langage clair et compréhensible par tous ; d’autre part – et c’est là sans doute sa grande originalité – il traite de l’aspect quantique des probabilités, qui sont à la base de développements plus récents en physique statistique et en théorie des champs. Il ne néglige pas pour autant les techniques de simulation aléatoire qui intéresseront aussi bien les milieux de la recherche que de l’industrie. Ce livre s’appuie sur la longue expérience d’enseignement de l’auteur auprès d’étudiants en master et de futurs ingénieurs. C’est à eux que l’ouvrage s’adresse en priorité, ainsi qu’aux élèves des classes préparatoires intéressés par les méthodes stochastiques. Des exercices corrigés complètent chaque chapitre et permettent une meilleure compréhension de leur contenu. Une importante bibliographie termine l’ouvrage, laissant au lecteur le loisir d’approfondir quelques-uns des plus beaux thèmes de ce vaste territoire aléatoire, qui est au cœur des préoccupations scientifiques d’aujourd’hui. Franck Jedrzejewski est chercheur au Commissariat à l’énergie atomique (CEA). Il est l’auteur chez Springer d’une Introduction aux méthodes numériques (2e éd., 2005). 

Distribution (Probability theory) --- Mathematical physics. --- Mathematics. --- Quantum theory. --- Quantum dynamics --- Quantum mechanics --- Quantum physics --- Physics --- Mechanics --- Thermodynamics --- Math --- Science --- Physical mathematics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Mathematics

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Stochastic processes are mathematical models of random phenomena that evolve according to prescribed dynamics. Processes commonly used in applications are Markov chains in discrete and continuous time, renewal and regenerative processes, Poisson processes, and Brownian motion. This volume gives an in-depth description of the structure and basic properties of these stochastic processes. A main focus is on equilibrium distributions, strong laws of large numbers, and ordinary and functional central limit theorems for cost and performance parameters. Although these results differ for various processes, they have a common trait of being limit theorems for processes with regenerative increments. Extensive examples and exercises show how to formulate stochastic models of systems as functions of a system’s data and dynamics, and how to represent and analyze cost and performance measures. Topics include stochastic networks, spatial and space-time Poisson processes, queueing, reversible processes, simulation, Brownian approximations, and varied Markovian models. The technical level of the volume is between that of introductory texts that focus on highlights of applied stochastic processes, and advanced texts that focus on theoretical aspects of processes. Intended readers are researchers and graduate students in mathematics, statistics, operations research, computer science, engineering, and business.

Automatic control -- Reliability. --- Probabilities. --- Stochastic processes. --- Stochastic processes --- Probabilities --- Mathematics --- Mathematical Statistics --- Physical Sciences & Mathematics --- Probability --- Statistical inference --- Random processes --- Mathematics. --- Mathematical models. --- Probability Theory and Stochastic Processes. --- Mathematical Modeling and Industrial Mathematics. --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Distribution (Probability theory. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Models, Mathematical --- Simulation methods

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Stochastic programming provides a framework for modelling, analyzing, and solving optimization problems with some parameters being not known up to a probability distribution. Such problems arise in a variety of applications, such as inventory control, financial planning and portfolio optimization, airline revenue management, scheduling and operation of power systems, and supply chain management. Christian Küchler studies various aspects of the stability of stochastic optimization problems as well as approximation and decomposition methods in stochastic programming. In particular, the author presents an extension of the Nested Benders decomposition algorithm related to the concept of recombining scenario trees. The approach combines the concept of cut sharing with a specific aggregation procedure and prevents an exponentially growing number of subproblem evaluations. Convergence results and numerical properties are discussed.

Decision-making. --- Stochastic programming. --- Uncertainty. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Theory --- Mathematics. --- Mathematical models. --- Probabilities. --- Mathematical Modeling and Industrial Mathematics. --- Probability Theory and Stochastic Processes. --- Mathematics, general. --- Linear programming --- Distribution (Probability theory. --- Math --- Science --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Models, Mathematical --- Simulation methods

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Over the last fifteen years fractal geometry has established itself as a substantial mathematical theory in its own right. The interplay between fractal geometry, analysis and stochastics has highly influenced recent developments in mathematical modeling of complicated structures. This process has been forced by problems in these areas related to applications in statistical physics, biomathematics and finance. This book is a collection of survey articles covering many of the most recent developments, like Schramm-Loewner evolution, fractal scaling limits, exceptional sets for percolation, and heat kernels on fractals. The authors were the keynote speakers at the conference "Fractal Geometry and Stochastics IV" at Greifswald in September 2008.

Fractals. --- Stochastic processes. --- Fractals --- Stochastic processes --- Mathematics --- Geometry --- Mathematical Statistics --- Physical Sciences & Mathematics --- Random processes --- Fractal geometry --- Fractal sets --- Geometry, Fractal --- Sets, Fractal --- Sets of fractional dimension --- Mathematics. --- Geometry. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Probabilities --- Dimension theory (Topology) --- Distribution (Probability theory. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Euclid's Elements --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk

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Over the last decade, spin glass theory has turned from a fascinating part of t- oretical physics to a ?ourishing and rapidly growing subject of probability theory as well. These developments have been triggered to a large part by the mathem- ical understanding gained on the fascinating and previously mysterious “Parisi solution” of the Sherrington–Kirkpatrick mean ?eld model of spin glasses, due to the work of Guerra, Talagrand, and others. At the same time, new aspects and applications of the methods developed there have come up. The presentvolumecollects a number of reviewsaswellas shorterarticlesby lecturers at a summer school on spin glasses that was held in July 2007 in Paris. These articles range from pedagogical introductions to state of the art papers, covering the latest developments. In their whole, they give a nice overview on the current state of the ?eld from the mathematical side. The review by Bovier and Kurkova gives a concise introduction to mean ?eld models, starting with the Curie–Weiss model and moving over the Random Energymodels up to the Parisisolutionof the Sherrington–Kirkpatrikmodel. Ben Arous and Kuptsov present a more recent view and disordered systems through the so-called local energy statistics. They emphasize that there are many ways to look at Hamiltonians of disordered systems that make appear the Random Energy model (or independent random variables) as a universal mechanism for describing certain rare events. An important tool in the analysis of spin glasses are correlation identities.

Glass -- Congresses. --- Spin glasses -- Congresses. --- Spin glasses -- Mathematical models. --- Spin glasses --- Mathematics --- Physics --- Atomic Physics --- Mathematical Statistics --- Physical Sciences & Mathematics --- Solid state physics. --- Glasses, Magnetic --- Glasses, Spin --- Magnetic glasses --- Mathematics. --- Probabilities. --- Physics. --- Probability Theory and Stochastic Processes. --- Mathematical Methods in Physics. --- Magnetic alloys --- Nuclear spin --- Solid state physics --- Solids --- Distribution (Probability theory. --- Mathematical physics. --- Physical mathematics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probabilities --- Natural philosophy --- Philosophy, Natural --- Physical sciences --- Dynamics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk