Sampling Algorithms

Over the last few decades, important progresses in the methods of sampling have been achieved. This book draws up an inventory of new methods that can be useful for selecting samples. Forty-six sampling methods are described in the framework of general theory. The algorithms are described rigorously, which allows implementing directly the described methods. This book is aimed at experienced statisticians who are familiar with the theory of survey sampling.Yves Tillé is a professor at the University of Neuchâtel (Switzerland)

Over the last few decades, important progresses in the methods of sampling have been achieved. This book draws up an inventory of new methods that can be useful for selecting samples. Forty-six sampling methods are described in the framework of general theory. The algorithms are described rigorously, which allows implementing directly the described methods. This book is aimed at experienced statisticians who are familiar with the theory of survey sampling.

The subject of these notes is counting and related topics, viewed from a computational perspective. A major theme of the book is the idea of accumulating information about a set of combinatorial structures by performing a random walk on those structures. These notes will be of value not only to teachers of postgraduate courses on these topics, but also to established researchers. For the first time this body of knowledge has been brought together in a single volume.

This book provides the reader with a background on simulating copulas and multivariate distributions in general. It unifies the scattered literature on the simulation of various families of copulas (elliptical, Archimedean, Marshall-Olkin type, etc.) as well as on different construction principles (factor models, pair-copula construction, etc.). The book is self-contained and unified in presentation and can be used as a textbook for advanced undergraduate or graduate students with a firm background in stochastics. Alongside the theoretical foundation, ready-to-implement algorithms and many examples make this book a valuable tool for anyone who is applying the methodology.

'The book remains a valuable tool both for statisticians who are already familiar with the theory of copulas and just need to develop sampling algorithms, and for practitioners who want to learn copulas and implement the simulation techniques needed to exploit the potential of copulas in applications.'Mathematical ReviewsThe book provides the background on simulating copulas and multivariate distributions in general. It unifies the scattered literature on the simulation of various families of copulas (elliptical, Archimedean, Marshall-Olkin type, etc.) as well as on different construction principles (factor models, pair-copula construction, etc.). The book is self-contained and unified in presentation and can be used as a textbook for graduate and advanced undergraduate students with a firm background in stochastics. Besides the theoretical foundation, ready-to-implement algorithms and many examples make the book a valuable tool for anyone who is applying the methodology.

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. This book brings the state-of-the-art research together for the first time. It provides practical modeling methods for many real-world problems with high dimensionality or complexity which have not hitherto been treatable with Markov decision processes.

Spectral methods refer to the use of eigenvalues, eigenvectors, singular values and singular vectors. They are widely used in Engineering, Applied Mathematics and Statistics. More recently, spectral methods have found numerous applications in Computer Science to "discrete" as well as "continuous" problems. Spectral Algorithms describes modern applications of spectral methods, and novel algorithms for estimating spectral parameters. The first part of the book presents applications of spectral methods to problems from a variety of topics including combinatorial optimization, learning and clustering. The second part of the book is motivated by efficiency considerations. A feature of many modern applications is the massive amount of input data. While sophisticated algorithms for matrix computations have been developed over a century, a more recent development is algorithms based on "sampling on the fly" from massive matrices. Good estimates of singular values and low rank approximations of the whole matrix can be provably derived from a sample. The main emphasis in the second part of the book is to present these sampling methods with rigorous error bounds. It also presents recent extensions of spectral methods from matrices to tensors and their applications to some combinatorial optimization problems.

First Published in 1987. This is the collected proceedings of the second International Conference on Genetic Algorithms held at the Massachusetts Institute of Technology, Cambridge, MA on the 28th to the 31st July 1987. With papers on Genetic search theory, Adaptive search operators, representation issues, connectionism and parallelism, credit assignment ad learning, and applications.

In this book, the latest achievements of the computation time analysis theory and practical applications of intelligent algorithms are set out. There are five chapters: (1) new method of intelligent algorithm computation time analysis; (2)Application of intelligent algorithms in computer vision; (3)Application of intelligent algorithms in logistics scheduling; (4)Application of intelligent algorithms in software testing; and (5) application of intelligent algorithm in multi-objective optimization. The content of each chapter is supported by papers published in top journals. The authors introduce the work of each part, which mainly includes a brief introduction (mainly for readers to understand) and academic discussion (rigorous theoretical and experimental support), in a vivid and interesting way through excellent pictures and literary compositions. To help readers learn and make progress together, each part of this book provides relevant literature, code, experimental data, and so on. Integrates the theoretical analysis results of intelligent algorithms, which is convenient for the majority of researchers to deeply understand the theoretical analysis results of intelligent algorithms and further supplement and improve the theoretical research of intelligent algorithms Opens up readers' understanding of the theoretical level of intelligent algorithms and spreads the inherent charm of intelligent algorithms Integrates the diverse knowledge of society and provides a more comprehensive and scientific knowledge of intelligent algorithm theory

In Algorithms and Data Structures for Massive Datasets, you'll discover methods for reducing and sketching data so it fits in small memory without losing accuracy, and unlock the algorithms and data structures that form the backbone of a big data system. Data structures and algorithms that are great for traditional software may quickly slow or fail altogether when applied to huge datasets. Algorithms and Data Structures for Massive Datasets introduces a toolbox of new techniques that are perfect for handling modern big data applications. In Algorithms and Data Structures for Massive Datasets, you'll discover methods for reducing and sketching data so it fits in small memory without losing accuracy, and unlock the algorithms and data structures that form the backbone of a big data system. Filled with fun illustrations and examples from real-world businesses, you'll learn how each of these complex techniques can be practically applied to maximize the accuracy and throughput of big data processing and analytics. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.