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Uniform Central Limit Theorems

Author: R. M. Dudley
Publisher: Cambridge University Press
ISBN: 0521461022
Size: 28.81 MB
Format: PDF, ePub, Mobi
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This treatise by an acknowledged expert includes several topics not found in any previous book.

Advanced Lectures On Machine Learning

Author: Shahar Mendelson
Publisher: Springer Science & Business Media
ISBN: 3540005293
Size: 26.86 MB
Format: PDF
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This book presents revised reviewed versions of lectures given during the Machine Learning Summer School held in Canberra, Australia, in February 2002. The lectures address the following key topics in algorithmic learning: statistical learning theory, kernel methods, boosting, reinforcement learning, theory learning, association rule learning, and learning linear classifier systems. Thus, the book is well balanced between classical topics and new approaches in machine learning. Advanced students and lecturers will find this book a coherent in-depth overview of this exciting area, while researchers will use this book as a valuable source of reference.

Subsampling

Author: Dimitris N. Politis
Publisher: Springer Science & Business Media
ISBN: 1461215544
Size: 44.54 MB
Format: PDF, Mobi
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Since Efron's profound paper on the bootstrap, an enormous amount of effort has been spent on the development of bootstrap, jacknife, and other resampling methods. The primary goal of these computer-intensive methods has been to provide statistical tools that work in complex situations without imposing unrealistic or unverifiable assumptions about the data generating mechanism. This book sets out to lay some of the foundations for subsampling methodology and related methods.

Modeling And Control In Air Conditioning Systems

Author: Ye Yao
Publisher: Springer
ISBN: 3662533138
Size: 34.29 MB
Format: PDF, ePub
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This book investigates the latest modeling and control technologies in the context of air-conditioning systems. Firstly, it introduces the state-space method for developing dynamic models of all components in a central air-conditioning system. The models are primarily nonlinear and based on the fundamental principle of energy and mass conservation, and are transformed into state-space form through linearization. The book goes on to describe and discuss the state-space models with the help of graph theory and the structure-matrix theory. Subsequently, virtual sensor calibration and virtual sensing methods (which are very useful for real system control) are illustrated together with a case study. Model-based predictive control and state-space feedback control are applied to air-conditioning systems to yield better local control, while the air-side synergic control scheme and a global optimization strategy based on the decomposition-coordination method are developed so as to achieve energy conservation in the central air-conditioning system. Lastly, control strategies for VAV systems including total air volume control and trim & response static pressure control are investigated in practice.

Markov Processes Gaussian Processes And Local Times

Author: Michael B. Marcus
Publisher: Cambridge University Press
ISBN: 1139458833
Size: 58.79 MB
Format: PDF, Docs
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This book was first published in 2006. Written by two of the foremost researchers in the field, this book studies the local times of Markov processes by employing isomorphism theorems that relate them to certain associated Gaussian processes. It builds to this material through self-contained but harmonized 'mini-courses' on the relevant ingredients, which assume only knowledge of measure-theoretic probability. The streamlined selection of topics creates an easy entrance for students and experts in related fields. The book starts by developing the fundamentals of Markov process theory and then of Gaussian process theory, including sample path properties. It then proceeds to more advanced results, bringing the reader to the heart of contemporary research. It presents the remarkable isomorphism theorems of Dynkin and Eisenbaum and then shows how they can be applied to obtain new properties of Markov processes by using well-established techniques in Gaussian process theory. This original, readable book will appeal to both researchers and advanced graduate students.

Real Analysis And Probability

Author: R. M. Dudley
Publisher: Cambridge University Press
ISBN: 9780521007542
Size: 10.18 MB
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This classic graduate textbook offers a clear exposition of modern probability theory and of the interplay between the properties of metric spaces and probability measures. The comprehensive historical notes have been further amplified for this new edition, and a number of new exercises have been added, together with hints for solution.

Probability

Author: Rick Durrett
Publisher: Cambridge University Press
ISBN: 113949113X
Size: 52.68 MB
Format: PDF, Kindle
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This classic introduction to probability theory for beginning graduate students covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems. The fourth edition begins with a short chapter on measure theory to orient readers new to the subject.

Differentiability Of Six Operators On Nonsmooth Functions And P Variation

Author: R. M. Dudley
Publisher: Springer Science & Business
ISBN: 9783540659754
Size: 27.49 MB
Format: PDF
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The book is about differentiability of six operators on functions or pairs of functions: composition (f of g), integration (of f dg), multiplication and convolution of two functions, both varying, and the product integral and inverse operators for one function. The operators are differentiable with respect to p-variation norms with optimal remainder bounds. Thus the functions as arguments of the operators can be nonsmooth, possibly discontinuous, but four of the six operators turn out to be analytic (holomorphic) for some p-variation norms. The reader will need to know basic real analysis, including Riemann and Lebesgue integration. The book is intended for analysts, statisticians and probabilists. Analysts and statisticians have each studied the differentiability of some of the operators from different viewpoints, and this volume seeks to unify and expand their results.