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Author: Steven K. Thompson Publisher: Wiley-Interscience ISBN: Category : Mathematics Languages : en Pages : 296
Book Description
Offering a viable solution to the long-standing problem of estimating the abundance of rare, clustered populations, adaptive sampling designs are rapidly gaining prominence in the natural and social sciences as well as in other fields with inherently difficult sampling situations. In marked contrast to conventional sampling designs, in which the entire sample of units to be observed is fixed prior to the survey, adaptive sampling strategies allow for increased sampling intensity depending upon observations made during the survey. For example, in a survey to assess the abundance of a rare animal species, neighboring sites may be added to the sample whenever the species is encountered during the survey. In an epidemiological survey of a contagious or genetically linked disease, sampling intensity may be increased whenever prevalence of the disease is encountered. Written by two acknowledged experts in this emerging field, this book offers researchers their first comprehensive introduction to adaptive sampling. An ideal reference for statisticians conducting research in survey designs and spatial statistics as well as researchers working in the environmental, ecological, public health, and biomedical sciences. Adaptive Sampling: Provides a comprehensive, fully integrated introduction to adaptive sampling theory and practice Describes recent research findings Introduces readers to a wide range of adaptive sampling strategies and techniques Includes numerous real-world examples from environmental pollution studies, surveys of rare animal and plant species, studies of contagious diseases, marketing surveys, mineral and fossil-fuel assessments, and more
Author: Steven K. Thompson Publisher: Wiley-Interscience ISBN: Category : Mathematics Languages : en Pages : 296
Book Description
Offering a viable solution to the long-standing problem of estimating the abundance of rare, clustered populations, adaptive sampling designs are rapidly gaining prominence in the natural and social sciences as well as in other fields with inherently difficult sampling situations. In marked contrast to conventional sampling designs, in which the entire sample of units to be observed is fixed prior to the survey, adaptive sampling strategies allow for increased sampling intensity depending upon observations made during the survey. For example, in a survey to assess the abundance of a rare animal species, neighboring sites may be added to the sample whenever the species is encountered during the survey. In an epidemiological survey of a contagious or genetically linked disease, sampling intensity may be increased whenever prevalence of the disease is encountered. Written by two acknowledged experts in this emerging field, this book offers researchers their first comprehensive introduction to adaptive sampling. An ideal reference for statisticians conducting research in survey designs and spatial statistics as well as researchers working in the environmental, ecological, public health, and biomedical sciences. Adaptive Sampling: Provides a comprehensive, fully integrated introduction to adaptive sampling theory and practice Describes recent research findings Introduces readers to a wide range of adaptive sampling strategies and techniques Includes numerous real-world examples from environmental pollution studies, surveys of rare animal and plant species, studies of contagious diseases, marketing surveys, mineral and fossil-fuel assessments, and more
Author: George A.F. Seber Publisher: Springer Science & Business Media ISBN: 3642336566 Category : Mathematics Languages : en Pages : 78
Book Description
This book aims to provide an overview of some adaptive techniques used in estimating parameters for finite populations where the sampling at any stage depends on the sampling information obtained to date. The sample adapts to new information as it comes in. These methods are especially used for sparse and clustered populations. Written by two acknowledged experts in the field of adaptive sampling.
Author: Reuven Y. Rubinstein Publisher: John Wiley & Sons ISBN: 1118632389 Category : Mathematics Languages : en Pages : 470
Book Description
This accessible new edition explores the major topics in Monte Carlo simulation that have arisen over the past 30 years and presents a sound foundation for problem solving Simulation and the Monte Carlo Method, Third Edition reflects the latest developments in the field and presents a fully updated and comprehensive account of the state-of-the-art theory, methods and applications that have emerged in Monte Carlo simulation since the publication of the classic First Edition over more than a quarter of a century ago. While maintaining its accessible and intuitive approach, this revised edition features a wealth of up-to-date information that facilitates a deeper understanding of problem solving across a wide array of subject areas, such as engineering, statistics, computer science, mathematics, and the physical and life sciences. The book begins with a modernized introduction that addresses the basic concepts of probability, Markov processes, and convex optimization. Subsequent chapters discuss the dramatic changes that have occurred in the field of the Monte Carlo method, with coverage of many modern topics including: Markov Chain Monte Carlo, variance reduction techniques such as importance (re-)sampling, and the transform likelihood ratio method, the score function method for sensitivity analysis, the stochastic approximation method and the stochastic counter-part method for Monte Carlo optimization, the cross-entropy method for rare events estimation and combinatorial optimization, and application of Monte Carlo techniques for counting problems. An extensive range of exercises is provided at the end of each chapter, as well as a generous sampling of applied examples. The Third Edition features a new chapter on the highly versatile splitting method, with applications to rare-event estimation, counting, sampling, and optimization. A second new chapter introduces the stochastic enumeration method, which is a new fast sequential Monte Carlo method for tree search. In addition, the Third Edition features new material on: • Random number generation, including multiple-recursive generators and the Mersenne Twister • Simulation of Gaussian processes, Brownian motion, and diffusion processes • Multilevel Monte Carlo method • New enhancements of the cross-entropy (CE) method, including the “improved” CE method, which uses sampling from the zero-variance distribution to find the optimal importance sampling parameters • Over 100 algorithms in modern pseudo code with flow control • Over 25 new exercises Simulation and the Monte Carlo Method, Third Edition is an excellent text for upper-undergraduate and beginning graduate courses in stochastic simulation and Monte Carlo techniques. The book also serves as a valuable reference for professionals who would like to achieve a more formal understanding of the Monte Carlo method. Reuven Y. Rubinstein, DSc, was Professor Emeritus in the Faculty of Industrial Engineering and Management at Technion-Israel Institute of Technology. He served as a consultant at numerous large-scale organizations, such as IBM, Motorola, and NEC. The author of over 100 articles and six books, Dr. Rubinstein was also the inventor of the popular score-function method in simulation analysis and generic cross-entropy methods for combinatorial optimization and counting. Dirk P. Kroese, PhD, is a Professor of Mathematics and Statistics in the School of Mathematics and Physics of The University of Queensland, Australia. He has published over 100 articles and four books in a wide range of areas in applied probability and statistics, including Monte Carlo methods, cross-entropy, randomized algorithms, tele-traffic c theory, reliability, computational statistics, applied probability, and stochastic modeling.
Author: Aske Plaat Publisher: Springer Nature ISBN: 3030592383 Category : Computers Languages : en Pages : 330
Book Description
In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI). After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.
Author: Arijit Chaudhuri Publisher: CRC Press ISBN: 1466577568 Category : Mathematics Languages : en Pages : 136
Book Description
Combining the two statistical techniques of network sampling and adaptive sampling, this book illustrates the advantages of using them in tandem to effectively capture sparsely located elements in unknown pockets. It shows how network sampling is a reliable guide in capturing inaccessible entities through linked auxiliaries. The text also explores how adaptive sampling is strengthened in information content through subsidiary sampling with devices to mitigate unmanageable expanding sample sizes. Empirical data illustrates the applicability of both methods.
Author: David G. Hankin Publisher: Oxford University Press ISBN: 0192547844 Category : Mathematics Languages : en Pages : 368
Book Description
Sampling theory considers how methods for selection of a subset of units from a finite population (a sample) affect the accuracy of estimates of descriptive population parameters (mean, total, proportion). Although a sound knowledge of sampling theory principles would seem essential for ecologists and natural resource scientists, the subject tends to be somewhat overlooked in contrast to other core statistical topics such as regression analysis, experimental design, and multivariate statistics. This introductory text aims to redress this imbalance by specifically targeting ecologists and resource scientists, and illustrating how sampling theory can be applied in a wide variety of resource contexts. The emphasis throughout is on design-based sampling from finite populations, but some attention is given to model-based prediction and sampling from infinite populations. Sampling Theory is an introductory textbook suitable for advanced undergraduates, graduate students, professional researchers, and practitioners in the fields of ecology, evolution, conservation biology, and natural resource sciences (including fisheries, wildlife, rangeland, ecology and forestry).
Author: Steven K. Thompson Publisher: John Wiley & Sons ISBN: 0470402318 Category : Mathematics Languages : en Pages : 470
Book Description
Praise for the Second Edition "This book has never had a competitor. It is the only book that takes a broad approach to sampling . . . any good personal statistics library should include a copy of this book." —Technometrics "Well-written . . . an excellent book on an important subject. Highly recommended." —Choice "An ideal reference for scientific researchers and other professionals who use sampling." —Zentralblatt Math Features new developments in the field combined with all aspects of obtaining, interpreting, and using sample data Sampling provides an up-to-date treatment of both classical and modern sampling design and estimation methods, along with sampling methods for rare, clustered, and hard-to-detect populations. This Third Edition retains the general organization of the two previous editions, but incorporates extensive new material—sections, exercises, and examples—throughout. Inside, readers will find all-new approaches to explain the various techniques in the book; new figures to assist in better visualizing and comprehending underlying concepts such as the different sampling strategies; computing notes for sample selection, calculation of estimates, and simulations; and more. Organized into six sections, the book covers basic sampling, from simple random to unequal probability sampling; the use of auxiliary data with ratio and regression estimation; sufficient data, model, and design in practical sampling; useful designs such as stratified, cluster and systematic, multistage, double and network sampling; detectability methods for elusive populations; spatial sampling; and adaptive sampling designs. Featuring a broad range of topics, Sampling, Third Edition serves as a valuable reference on useful sampling and estimation methods for researchers in various fields of study, including biostatistics, ecology, and the health sciences. The book is also ideal for courses on statistical sampling at the upper-undergraduate and graduate levels.
Author: David Hankin Publisher: Oxford University Press, USA ISBN: 0198815794 Category : Mathematics Languages : en Pages : 360
Book Description
Sampling theory considers how methods for selection of a subset of units from a finite population (a sample) affect the accuracy of estimates of descriptive population parameters (mean, total, proportion). Although a sound knowledge of sampling theory principles would seem essential for ecologists and natural resource scientists, the subject tends to be somewhat overlooked in contrast to other core statistical topics such as regression analysis, experimental design, and multivariate statistics. This introductory text aims to redress this imbalance by specifically targeting ecologists and resource scientists, and illustrating how sampling theory can be applied in a wide variety of resource contexts. The emphasis throughout is on design-based sampling from finite populations, but some attention is given to model-based prediction and sampling from infinite populations.
Author: Steven K. Thompson
Author: Steven K. Thompson
Author: George A.F. Seber
Author: Reuven Y. Rubinstein
Author: Klaus Fiedler
Author: Aske Plaat
Author: Arijit Chaudhuri
Author: David G. Hankin
Author: Steven K. Thompson
Author: David Hankin
Author: George Anthony Rahe