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    • VLSI Electronics: Microstructure

      • 1st Edition
      • Volume 22
      • July 10, 2014
      • Anant G. Sabnis
      • English
      • Paperback
        9 7 8 1 4 9 3 3 0 6 9 8 5
      • eBook
        9 7 8 1 4 8 3 2 9 6 5 8 6
      As integrated cicuits become more complex, with smaller and smaller geometries, much more care must be taken to avoid reliability problems. This practical volume covers a broad spectrum of reliability issues in integrated circuits, from basic concepts to packaging.

    • Machine Vision

      • 1st Edition
      • July 10, 2014
      • E. R. Davies
      • P. G. Farrell + 1 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 3 7 9 5 4
      • eBook
        9 7 8 1 4 8 3 2 7 5 6 1 1
      Machine Vision: Theory, Algorithms, Practicalities covers the limitations, constraints, and tradeoffs of vision algorithms. This book is organized into four parts encompassing 21 chapters that tackle general topics, such as noise suppression, edge detection, principles of illumination, feature recognition, Bayes’ theory, and Hough transforms. Part 1 provides research ideas on imaging and image filtering operations, thresholding techniques, edge detection, and binary shape and boundary pattern analyses. Part 2 deals with the area of intermediate-level vision, the nature of the Hough transform, shape detection, and corner location. Part 3 demonstrates some of the practical applications of the basic work previously covered in the book. This part also discusses some of the principles underlying implementation, including on lighting and hardware systems. Part 4 highlights the limitations and constraints of vision algorithms and their corresponding solutions. This book will prove useful to students with undergraduate course on vision for electronic engineering or computer science.

    • Probability Inequalities in Multivariate Distributions

      • 1st Edition
      • July 10, 2014
      • Y. L. Tong
      • Z. W. Birnbaum + 1 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 4 7 6 1 8
      • eBook
        9 7 8 1 4 8 3 2 6 9 2 1 4
      Probability Inequalities in Multivariate Distributions is a comprehensive treatment of probability inequalities in multivariate distributions, balancing the treatment between theory and applications. The book is concerned only with those inequalities that are of types T1-T5. The conditions for such inequalities range from very specific to very general. Comprised of eight chapters, this volume begins by presenting a classification of probability inequalities, followed by a discussion on inequalities for multivariate normal distribution as well as their dependence on correlation coefficients. The reader is then introduced to inequalities for other well-known distributions, including the multivariate distributions of t, chi-square, and F; inequalities for a class of symmetric unimodal distributions and for a certain class of random variables that are positively dependent by association or by mixture; and inequalities obtainable through the mathematical tool of majorization and weak majorization. The book also describes some distribution-free inequalities before concluding with an overview of their applications in simultaneous confidence regions, hypothesis testing, multiple decision problems, and reliability and life testing. This monograph is intended for mathematicians, statisticians, students, and those who are primarily interested in inequalities.

    • Mathematical Statistics

      • 1st Edition
      • July 10, 2014
      • Thomas S. Ferguson
      • Z. W. Birnbaum + 1 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 0 7 8 0 3
      • eBook
        9 7 8 1 4 8 3 2 2 1 2 3 6
      Mathematical Statistics: A Decision Theoretic Approach presents an investigation of the extent to which problems of mathematical statistics may be treated by decision theory approach. This book deals with statistical theory that could be justified from a decision-theoretic viewpoint. Organized into seven chapters, this book begins with an overview of the elements of decision theory that are similar to those of the theory of games. This text then examines the main theorems of decision theory that involve two more notions, namely the admissibility of a decision rule and the completeness of a class of decision rules. Other chapters consider the development of theorems in decision theory that are valid in general situations. This book discusses as well the invariance principle that involves groups of transformations over the three spaces around which decision theory is built. The final chapter deals with sequential decision problems. This book is a valuable resource for first-year graduate students in mathematics.

    • Introduction to Stochastic Dynamic Programming

      • 1st Edition
      • July 10, 2014
      • Sheldon M. Ross
      • Z. W. Birnbaum + 1 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 4 5 7 7 5
      • eBook
        9 7 8 1 4 8 3 2 6 9 0 9 2
      Introduction to Stochastic Dynamic Programming presents the basic theory and examines the scope of applications of stochastic dynamic programming. The book begins with a chapter on various finite-stage models, illustrating the wide range of applications of stochastic dynamic programming. Subsequent chapters study infinite-stage models: discounting future returns, minimizing nonnegative costs, maximizing nonnegative returns, and maximizing the long-run average return. Each of these chapters first considers whether an optimal policy need exist—providing counterexamples where appropriate—and then presents methods for obtaining such policies when they do. In addition, general areas of application are presented. The final two chapters are concerned with more specialized models. These include stochastic scheduling models and a type of process known as a multiproject bandit. The mathematical prerequisites for this text are relatively few. No prior knowledge of dynamic programming is assumed and only a moderate familiarity with probability— including the use of conditional expectation—is necessary.

    • Stochastic Calculus and Stochastic Models

      • 1st Edition
      • July 10, 2014
      • E. J. McShane
      • Z. W. Birnbaum + 1 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 0 5 3 4 2
      • eBook
        9 7 8 1 4 8 3 2 1 8 7 7 9
      Probability and Mathematical Statistics: A Series of Monographs and Textbooks: Stochastic Calculus and Stochastic Models focuses on the properties, functions, and applications of stochastic integrals. The publication first ponders on stochastic integrals, existence of stochastic integrals, and continuity, chain rule, and substitution. Discussions focus on differentiation of a composite function, continuity of sample functions, existence and vanishing of stochastic integrals, canonical form, elementary properties of integrals, and the Itô-belated integral. The book then examines stochastic differential equations, including existence of solutions of stochastic differential equations, linear differential equations and their adjoints, approximation lemma, and the Cauchy-Maruyama approximation. The manuscript takes a look at equations in canonical form, as well as justification of the canonical extension in stochastic modeling; rate of convergence of approximations to solutions; comparison of ordinary and stochastic differential equations; and invariance under change of coordinates. The publication is a dependable reference for mathematicians and researchers interested in stochastic integrals.

    • Foundations of Stochastic Analysis

      • 1st Edition
      • July 10, 2014
      • M. M. Rao
      • Z. W. Birnbaum + 1 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 4 5 1 6 4
      • eBook
        9 7 8 1 4 8 3 2 6 9 3 1 3
      Foundations of Stochastic Analysis deals with the foundations of the theory of Kolmogorov and Bochner and its impact on the growth of stochastic analysis. Topics covered range from conditional expectations and probabilities to projective and direct limits, as well as martingales and likelihood ratios. Abstract martingales and their applications are also discussed. Comprised of five chapters, this volume begins with an overview of the basic Kolmogorov-Bochner theorem, followed by a discussion on conditional expectations and probabilities containing several characterizations of operators and measures. The applications of these conditional expectations and probabilities to Reynolds operators are also considered. The reader is then introduced to projective limits, direct limits, and a generalized Kolmogorov existence theorem, along with infinite product conditional probability measures. The book also considers martingales and their applications to likelihood ratios before concluding with a description of abstract martingales and their applications to convergence and harmonic analysis, as well as their relation to ergodic theory. This monograph should be of considerable interest to researchers and graduate students working in stochastic analysis.

    • Information Theory

      • 1st Edition
      • July 10, 2014
      • Imre Csiszár + 1 more
      • Z. W. Birnbaun + 1 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 3 7 8 2 4
      • eBook
        9 7 8 1 4 8 3 2 8 1 5 7 5
      Information Theory: Coding Theorems for Discrete Memoryless Systems presents mathematical models that involve independent random variables with finite range. This three-chapter text specifically describes the characteristic phenomena of information theory. Chapter 1 deals with information measures in simple coding problems, with emphasis on some formal properties of Shannon’s information and the non-block source coding. Chapter 2 describes the properties and practical aspects of the two-terminal systems. This chapter also examines the noisy channel coding problem, the computation of channel capacity, and the arbitrarily varying channels. Chapter 3 looks into the theory and practicality of multi-terminal systems. This book is intended primarily for graduate students and research workers in mathematics, electrical engineering, and computer science.

    • Measure and Integral

      • 1st Edition
      • July 10, 2014
      • Konrad Jacobs
      • Z. W. Birnbaum + 1 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 4 1 0 4 3
      • eBook
        9 7 8 1 4 8 3 2 6 3 0 4 5
      Probability and Mathematical Statistics: Measure and Integral provides information pertinent to the general mathematical notions and notations. This book discusses how the machinery of ?-extension works and how ?-content is derived from ?-measure. Organized into 16 chapters, this book begins with an overview of the classical Hahn–Banach theorem and introduces the Banach limits in the form of a major exercise. This text then presents the Daniell extension theory for positive ?-measures. Other chapters consider the transform of ?-contents and ?-measures by measurable mappings and kernels. This text is also devoted to a thorough study of the vector lattice of signed contents. This book discusses as well an abstract regularity theory and applied to the standard cases of compact, locally compact, and Polish spaces. The final chapter deals with the rudiments of the Krein–Milman theorem, along with some of their applications. This book is a valuable resource for graduate students.

    • Martingale Limit Theory and Its Application

      • 1st Edition
      • July 10, 2014
      • P. Hall + 1 more
      • Z. W. Birnbaum + 1 more
      • English
      • Paperback
        9 7 8 1 4 8 3 2 4 0 2 4 4
      • eBook
        9 7 8 1 4 8 3 2 6 3 2 2 9
      Martingale Limit Theory and Its Application discusses the asymptotic properties of martingales, particularly as regards key prototype of probabilistic behavior that has wide applications. The book explains the thesis that martingale theory is central to probability theory, and also examines the relationships between martingales and processes embeddable in or approximated by Brownian motion. The text reviews the martingale convergence theorem, the classical limit theory and analogs, and the martingale limit theorems viewed as the rate of convergence results in the martingale convergence theorem. The book explains the square function inequalities, weak law of large numbers, as well as the strong law of large numbers. The text discusses the reverse martingales, martingale tail sums, the invariance principles in the central limit theorem, and also the law of the iterated logarithm. The book investigates the limit theory for stationary processes via corresponding results for approximating martingales and the estimation of parameters from stochastic processes. The text can be profitably used as a reference for mathematicians, advanced students, and professors of higher mathematics or statistics.

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