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The study of quantum disorder has generated considerable research activity in mathematics and physics over past 40 years. While single-particle models have been extensively studied at a rigorous mathematical level, little was known about systems of several interacting particles, let alone systems with positive spatial particle density. Creating a consistent theory of disorder in multi-particle quantum systems is an important and challenging problem that largely remains open. Multi-scale Analysis for Random Quantum Systems with Interaction presents the progress that had been recently achieved in this area. The main focus of the book is on a rigorous derivation of the multi-particle localization in a strong random external potential field. To make the presentation accessible to a wider audience, the authors restrict attention to a relatively simple tight-binding Anderson model on a cubic lattice Zd. This book includes the following cutting-edge features: * an introduction to the state-of-the-art single-particle localization theory * an extensive discussion of relevant technical aspects of the localization theory * a thorough comparison of the multi-particle model with its single-particle counterpart * a self-contained rigorous derivation of both spectral and dynamical localization in the multi-particle tight-binding Anderson model. Required mathematical background for the book includes a knowledge of functional calculus, spectral theory (essentially reduced to the case of finite matrices) and basic probability theory. This is an excellent text for a year-long graduate course or seminar in mathematical physics. It also can serve as a standard reference for specialists.
Solid state physics. --- Functional analysis. --- Mathematics. --- Distribution (Probability theory) --- Mathematical physics. --- Mathematical Methods in Physics. --- Probability Theory and Stochastic Processes. --- Applications of Mathematics. --- Spectroscopy and Microscopy. --- Multiscale modeling --- Localization theory --- Functional analysis --- Anderson model --- Mathematics --- Physical Sciences & Mathematics --- Calculus --- Model, Anderson --- Functional calculus --- Multi-scale modeling --- Multiscale models --- Physical mathematics --- Physics --- Distribution functions --- Frequency distribution --- Math --- Applied mathematics. --- Engineering mathematics. --- Probabilities. --- Physics. --- Spectroscopy. --- Microscopy. --- Functional Analysis. --- Solid State Physics. --- Distribution (Probability theory. --- Calculus of variations --- Functional equations --- Integral equations --- Science --- Characteristic functions --- Probabilities --- Anderson model. --- Localization theory. --- Multiscale modeling. --- Analysis, Microscopic --- Light microscopy --- Micrographic analysis --- Microscope and microscopy --- Microscopic analysis --- Optical microscopy --- Optics --- Analysis, Spectrum --- Spectra --- Spectrochemical analysis --- Spectrochemistry --- Spectrometry --- Spectroscopy --- Chemistry, Analytic --- Interferometry --- Radiation --- Wave-motion, Theory of --- Absorption spectra --- Light --- Spectroscope --- Solids --- Engineering --- Engineering analysis --- Mathematical analysis --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Mathematical statistics --- Risk --- Natural philosophy --- Philosophy, Natural --- Physical sciences --- Dynamics --- Qualitative --- Analytical chemistry
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Probability and Statistics are as much about intuition and problem solving as they are about theorem proving. Because of this, students can find it very difficult to make a successful transition from lectures to examinations to practice, since the problems involved can vary so much in nature. Since the subject is critical in many modern applications such as mathematical finance, quantitative management, telecommunications, signal processing, bioinformatics, as well as traditional ones such as insurance, social science andengineering, the authors have rectified deficiencies in traditional lecture-based methods by collecting together a wealth of exercises with complete solutions, adapted to needs and skills of students. Following on from the success of Probability and Statistics by Example: Basic Probability and Statistics, the authors here concentrate on random processes, particularly Markov processes, emphasising modelsrather than general constructions. Basic mathematical facts are supplied as and when they are needed andhistorical information is sprinkled throughout.
Stochastic processes --- Mathematical statistics --- Probabilities. --- Mathematical statistics. --- Probabilities --- Probabilités --- Statistique mathématique --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Sampling (Statistics) --- Probability --- Combinations --- Chance --- Least squares --- Risk --- Statistical methods --- Statistique mathématique --- Probabilités. --- Distribution (théorie des probabilités) --- Distribution (Probability theory) --- Markov, Processus de --- Markov processes --- Statistique mathématique. --- Distribution (théorie des probabilités) --- Markov processes. --- Statistique mathematique
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