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) As this book perhaps (and hopefully) will be read by readers with widely diff- ing backgrounds, some general advice may be useful: Don’t be put off if paragraphs, comments, or remarks contain material of a seemingly more technical nature that you don’t understand.
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Examples abound throughout remote sensing (satellite data mapping, data assimilation, climate-change studies, land use), medical imaging (organ... Læs mere
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Authored by two of the field's most prominent researchers, this new edition has been comprehensively updated, with new material on contemporary models and methods including stochastic DEA models, material on Sharpe-ratio, and asset liability management.
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This book provides a versatile and lucid treatment of classic as well as modern probability theory, while integrating them with core topics in statistical theory and also some key tools in machine learning.
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Monte Carlo simulation is one of the best tools for performing realistic analysis of complex systems as it allows most of the limiting assumptions on system behavior to be relaxed.
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Nonlinear Stochastic Processes addresses the frequently-encountered problem of incomplete information. Divided into three parts, the text begins with a focus on H8 filtering and control problems associated with general classes of nonlinear stochastic discrete-time systems.
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Motivation comes from everyday experiences of probability, such as that of a dice or cards, the idea of fairness in games of chance, and the random ways in which, say, birthdays are... Læs mere
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Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences.
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The featured review of the AMS describes the author’s earlier work in the field of approach spaces as, ‘A landmark in the history of general topology’. The theory... Læs mere
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Decoupling theory provides a general framework for analyzing problems involving dependent random variables as if they were independent. In particular, decoupling is an essential component in the development of the asymptotic theory of U-- statistics and U--processes.
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All other articles (including those of his contributions to Mathematical Reviews which go beyond a simple reporting of contents of articles) have been reproduced as they appeared, together with annotations and corrections made by Wassily on some private copies of his papers.
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This book is about using graphs to understand the relationship between a regression model and the data to which it is fitted. The methods developed in this book reveal how the fitted regression model depends on individual observations and on groups of observations.