Download or read book Sampling Algorithms written by Yves Tillé and published by Springer Science & Business Media. This book was released on 2006-03-28 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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)
Download or read book Counting Sampling and Integrating Algorithms and Complexity written by Mark Jerrum and published by Birkhäuser. This book was released on 2012-12-06 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Simulating Copulas Stochastic Models Sampling Algorithms And Applications Second Edition written by Scherer Matthias and published by #N/A. This book was released on 2017-06-07 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 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.
Download or read book Simulating Copulas Stochastic Models Sampling Algorithms And Applications written by Matthias Scherer and published by World Scientific. This book was released on 2012-06-26 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks written by Yunfei Xu and published by Springer. This book was released on 2015-10-27 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: This brief introduces a class of problems and models for the prediction of the scalar field of interest from noisy observations collected by mobile sensor networks. It also introduces the problem of optimal coordination of robotic sensors to maximize the prediction quality subject to communication and mobility constraints either in a centralized or distributed manner. To solve such problems, fully Bayesian approaches are adopted, allowing various sources of uncertainties to be integrated into an inferential framework effectively capturing all aspects of variability involved. The fully Bayesian approach also allows the most appropriate values for additional model parameters to be selected automatically by data, and the optimal inference and prediction for the underlying scalar field to be achieved. In particular, spatio-temporal Gaussian process regression is formulated for robotic sensors to fuse multifactorial effects of observations, measurement noise, and prior distributions for obtaining the predictive distribution of a scalar environmental field of interest. New techniques are introduced to avoid computationally prohibitive Markov chain Monte Carlo methods for resource-constrained mobile sensors. Bayesian Prediction and Adaptive Sampling Algorithms for Mobile Sensor Networks starts with a simple spatio-temporal model and increases the level of model flexibility and uncertainty step by step, simultaneously solving increasingly complicated problems and coping with increasing complexity, until it ends with fully Bayesian approaches that take into account a broad spectrum of uncertainties in observations, model parameters, and constraints in mobile sensor networks. The book is timely, being very useful for many researchers in control, robotics, computer science and statistics trying to tackle a variety of tasks such as environmental monitoring and adaptive sampling, surveillance, exploration, and plume tracking which are of increasing currency. Problems are solved creatively by seamless combination of theories and concepts from Bayesian statistics, mobile sensor networks, optimal experiment design, and distributed computation.
Download or read book Simulation based Algorithms for Markov Decision Processes written by Hyeong Soo Chang and published by Springer Science & Business Media. This book was released on 2007-05-01 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Genetic Algorithms and their Applications written by John J. Grefenstette and published by Psychology Press. This book was released on 2013-08-21 with total page 629 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Intelligent Algorithms written by Han Huang and published by Elsevier. This book was released on 2024-05-25 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Artificial Intelligence and Algorithms in Intelligent Systems written by Radek Silhavy and published by Springer. This book was released on 2018-05-26 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the latest trends and approaches in artificial intelligence research and its application to intelligent systems. It discusses hybridization of algorithms, new trends in neural networks, optimisation algorithms and real-life issues related to the application of artificial methods. The book constitutes the second volume of the refereed proceedings of the Artificial Intelligence and Algorithms in Intelligent Systems of the 7th Computer Science On-line Conference 2018 (CSOC 2018), held online in April 2018.
Download or read book Algorithms and Data Structures for Massive Datasets written by Dzejla Medjedovic and published by Simon and Schuster. This book was released on 2022-07-05 with total page 302 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Spectral Algorithms written by Ravindran Kannan and published by Now Publishers Inc. This book was released on 2009 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Algorithms For Big Data written by Moran Feldman and published by World Scientific. This book was released on 2020-07-13 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This unique volume is an introduction for computer scientists, including a formal study of theoretical algorithms for Big Data applications, which allows them to work on such algorithms in the future. It also serves as a useful reference guide for the general computer science population, providing a comprehensive overview of the fascinating world of such algorithms.To achieve these goals, the algorithmic results presented have been carefully chosen so that they demonstrate the important techniques and tools used in Big Data algorithms, and yet do not require tedious calculations or a very deep mathematical background.
Download or read book Algorithms Probability Networks and Games written by Christos Zaroliagis and published by Springer. This book was released on 2015-09-07 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Festschrift volume is published in honor of Professor Paul G. Spirakis on the occasion of his 60th birthday. It celebrates his significant contributions to computer science as an eminent, talented, and influential researcher and most visionary thought leader, with a great talent in inspiring and guiding young researchers. The book is a reflection of his main research activities in the fields of algorithms, probability, networks, and games, and contains a biographical sketch as well as essays and research contributions from close collaborators and former PhD students.
Download or read book A Photon Phase Space Source Model Incorporating Efficient Sampling Algorithms for Clinical Radiotherapy Treatment Planning Using the Monte Carlo Method written by Indrin Chetty and published by . This book was released on 1999 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book New Algorithms for Macromolecular Simulation written by Benedict Leimkuhler and published by Springer Science & Business Media. This book was released on 2006-03-22 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Molecular simulation is a widely used tool in biology, chemistry, physics and engineering. This book contains a collection of articles by leading researchers who are developing new methods for molecular modelling and simulation. Topics addressed here include: multiscale formulations for biomolecular modelling, such as quantum-classical methods and advanced solvation techniques; protein folding methods and schemes for sampling complex landscapes; membrane simulations; free energy calculation; and techniques for improving ergodicity. The book is meant to be useful for practitioners in the simulation community and for those new to molecular simulation who require a broad introduction to the state of the art.
Download or read book The Practical Handbook of Genetic Algorithms written by Lance D. Chambers and published by CRC Press. This book was released on 2019-09-17 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: The mathematics employed by genetic algorithms (GAs)are among the most exciting discoveries of the last few decades. But what exactly is a genetic algorithm? A genetic algorithm is a problem-solving method that uses genetics as its model of problem solving. It applies the rules of reproduction, gene crossover, and mutation to pseudo-organism
Download or read book Stochastic Algorithms Foundations and Applications written by Kathleen Steinhöfel and published by Springer. This book was released on 2003-07-31 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: SAGA 2001, the ?rst Symposium on Stochastic Algorithms, Foundations and Applications, took place on December 13–14, 2001 in Berlin, Germany. The present volume comprises contributed papers and four invited talks that were included in the ?nal program of the symposium. Stochastic algorithms constitute a general approach to ?nding approximate solutions to a wide variety of problems. Although there is no formal proof that stochastic algorithms perform better than deterministic ones, there is evidence by empirical observations that stochastic algorithms produce for a broad range of applications near-optimal solutions in a reasonable run-time. The symposium aims to provide a forum for presentation of original research in the design and analysis, experimental evaluation, and real-world application of stochastic algorithms. It focuses, in particular, on new algorithmic ideas invo- ing stochastic decisions and exploiting probabilistic properties of the underlying problem domain. The program of the symposium re?ects the e?ort to promote cooperation among practitioners and theoreticians and among algorithmic and complexity researchers of the ?eld. In this context, we would like to express our special gratitude to DaimlerChrysler AG for supporting SAGA 2001. The contributed papers included in the proceedings present results in the following areas: Network and distributed algorithms; local search methods for combinatorial optimization with application to constraint satisfaction problems, manufacturing systems, motor control unit calibration, and packing ?exible - jects; and computational learning theory.
