Randomness and Optimal Estimation in Data Sampling
Author | : M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. Smarandache |
Publisher | : Infinite Study |
Total Pages | : 63 |
Release | : 2007 |
Genre | : Estimation theory |
ISBN | : 1931233683 |
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Author | : M. Khoshnevisan, S. Saxena, H. P. Singh, S. Singh, F. Smarandache |
Publisher | : Infinite Study |
Total Pages | : 63 |
Release | : 2007 |
Genre | : Estimation theory |
ISBN | : 1931233683 |
Author | : National Research Council |
Publisher | : National Academies Press |
Total Pages | : 191 |
Release | : 2013-09-03 |
Genre | : Mathematics |
ISBN | : 0309287812 |
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.
Author | : William M. Bolstad |
Publisher | : John Wiley & Sons |
Total Pages | : 353 |
Release | : 2013-06-05 |
Genre | : Mathematics |
ISBN | : 1118619218 |
Praise for the First Edition "I cannot think of a better book for teachers of introductory statistics who want a readable and pedagogically sound text to introduce Bayesian statistics." —Statistics in Medical Research "[This book] is written in a lucid conversational style, which is so rare in mathematical writings. It does an excellent job of presenting Bayesian statistics as a perfectly reasonable approach to elementary problems in statistics." —STATS: The Magazine for Students of Statistics, American Statistical Association "Bolstad offers clear explanations of every concept and method making the book accessible and valuable to undergraduate and graduate students alike." —Journal of Applied Statistics The use of Bayesian methods in applied statistical analysis has become increasingly popular, yet most introductory statistics texts continue to only present the subject using frequentist methods. Introduction to Bayesian Statistics, Second Edition focuses on Bayesian methods that can be used for inference, and it also addresses how these methods compare favorably with frequentist alternatives. Teaching statistics from the Bayesian perspective allows for direct probability statements about parameters, and this approach is now more relevant than ever due to computer programs that allow practitioners to work on problems that contain many parameters. This book uniquely covers the topics typically found in an introductory statistics book—but from a Bayesian perspective—giving readers an advantage as they enter fields where statistics is used. This Second Edition provides: Extended coverage of Poisson and Gamma distributions Two new chapters on Bayesian inference for Poisson observations and Bayesian inference for the standard deviation for normal observations A twenty-five percent increase in exercises with selected answers at the end of the book A calculus refresher appendix and a summary on the use of statistical tables New computer exercises that use R functions and Minitab® macros for Bayesian analysis and Monte Carlo simulations Introduction to Bayesian Statistics, Second Edition is an invaluable textbook for advanced undergraduate and graduate-level statistics courses as well as a practical reference for statisticians who require a working knowledge of Bayesian statistics.
Author | : Aris Spanos |
Publisher | : Cambridge University Press |
Total Pages | : 848 |
Release | : 1999-09-02 |
Genre | : Business & Economics |
ISBN | : 9780521424080 |
This major new textbook is intended for students taking introductory courses in Probability Theory and Statistical Inference. The primary objective of this book is to establish the framework for the empirical modelling of observational (non-experimental) data. The text is extremely student-friendly, with pathways designed for semester usage, and although aimed primarily at students at second-year undergraduate level and above studying econometrics and economics, Probability Theory and Statistical Inference will also be useful for students in other disciplines which make extensive use of observational data, including Finance, Biology, Sociology and Psychology.
Author | : The Analytic Sciences Corporation |
Publisher | : MIT Press |
Total Pages | : 388 |
Release | : 1974-05-15 |
Genre | : Computers |
ISBN | : 9780262570480 |
This is the first book on the optimal estimation that places its major emphasis on practical applications, treating the subject more from an engineering than a mathematical orientation. Even so, theoretical and mathematical concepts are introduced and developed sufficiently to make the book a self-contained source of instruction for readers without prior knowledge of the basic principles of the field. The work is the product of the technical staff of The Analytic Sciences Corporation (TASC), an organization whose success has resulted largely from its applications of optimal estimation techniques to a wide variety of real situations involving large-scale systems. Arthur Gelb writes in the Foreword that "It is our intent throughout to provide a simple and interesting picture of the central issues underlying modern estimation theory and practice. Heuristic, rather than theoretically elegant, arguments are used extensively, with emphasis on physical insights and key questions of practical importance." Numerous illustrative examples, many based on actual applications, have been interspersed throughout the text to lead the student to a concrete understanding of the theoretical material. The inclusion of problems with "built-in" answers at the end of each of the nine chapters further enhances the self-study potential of the text. After a brief historical prelude, the book introduces the mathematics underlying random process theory and state-space characterization of linear dynamic systems. The theory and practice of optimal estimation is them presented, including filtering, smoothing, and prediction. Both linear and non-linear systems, and continuous- and discrete-time cases, are covered in considerable detail. New results are described concerning the application of covariance analysis to non-linear systems and the connection between observers and optimal estimators. The final chapters treat such practical and often pivotal issues as suboptimal structure, and computer loading considerations. This book is an outgrowth of a course given by TASC at a number of US Government facilities. Virtually all of the members of the TASC technical staff have, at one time and in one way or another, contributed to the material contained in the work.
Author | : Miloš Železný |
Publisher | : Springer |
Total Pages | : 383 |
Release | : 2013-08-24 |
Genre | : Computers |
ISBN | : 3319019317 |
This book constitutes the refereed proceedings of the 15th International Conference on Speech and Computer, SPECOM 2013, held in Pilsen, Czech Republic. The 48 revised full papers presented were carefully reviewed and selected from 90 initial submissions. The papers are organized in topical sections on speech recognition and understanding, spoken language processing, spoken dialogue systems, speaker identification and diarization, speech forensics and security, language identification, text-to-speech systems, speech perception and speech disorders, multimodal analysis and synthesis, understanding of speech and text, and audio-visual speech processing.
Author | : Robert M. Gray |
Publisher | : Cambridge University Press |
Total Pages | : 479 |
Release | : 2004-12-02 |
Genre | : Technology & Engineering |
ISBN | : 1139456288 |
This book describes the essential tools and techniques of statistical signal processing. At every stage theoretical ideas are linked to specific applications in communications and signal processing using a range of carefully chosen examples. The book begins with a development of basic probability, random objects, expectation, and second order moment theory followed by a wide variety of examples of the most popular random process models and their basic uses and properties. Specific applications to the analysis of random signals and systems for communicating, estimating, detecting, modulating, and other processing of signals are interspersed throughout the book. Hundreds of homework problems are included and the book is ideal for graduate students of electrical engineering and applied mathematics. It is also a useful reference for researchers in signal processing and communications.
Author | : Yves Tille |
Publisher | : John Wiley & Sons |
Total Pages | : 447 |
Release | : 2020-03-30 |
Genre | : Mathematics |
ISBN | : 0470682051 |
A much-needed reference on survey sampling and its applications that presents the latest advances in the field Seeking to show that sampling theory is a living discipline with a very broad scope, this book examines the modern development of the theory of survey sampling and the foundations of survey sampling. It offers readers a critical approach to the subject and discusses putting theory into practice. It also explores the treatment of non-sampling errors featuring a range of topics from the problems of coverage to the treatment of non-response. In addition, the book includes real examples, applications, and a large set of exercises with solutions. Sampling and Estimation from Finite Populations begins with a look at the history of survey sampling. It then offers chapters on: population, sample, and estimation; simple and systematic designs; stratification; sampling with unequal probabilities; balanced sampling; cluster and two-stage sampling; and other topics on sampling, such as spatial sampling, coordination in repeated surveys, and multiple survey frames. The book also includes sections on: post-stratification and calibration on marginal totals; calibration estimation; estimation of complex parameters; variance estimation by linearization; and much more. Provides an up-to-date review of the theory of sampling Discusses the foundation of inference in survey sampling, in particular, the model-based and design-based frameworks Reviews the problems of application of the theory into practice Also deals with the treatment of non sampling errors Sampling and Estimation from Finite Populations is an excellent book for methodologists and researchers in survey agencies and advanced undergraduate and graduate students in social science, statistics, and survey courses.
Author | : Xue-Bo Jin |
Publisher | : MDPI |
Total Pages | : 569 |
Release | : 2018-06-26 |
Genre | : Technology & Engineering |
ISBN | : 3038429333 |
This book is a printed edition of the Special Issue "Advances in Multi-Sensor Information Fusion: Theory and Applications 2017" that was published in Sensors