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The Empirical Bayes Two Action Rules With Floating Optimal Sample Size And Exponential Conditional Distributions
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Book Synopsis The Empirical Bayes Two-action Rules with Floating Optimal Sample Size and Exponential Conditional Distributions by : Pekka Laippala
Download or read book The Empirical Bayes Two-action Rules with Floating Optimal Sample Size and Exponential Conditional Distributions written by Pekka Laippala and published by . This book was released on 1980 with total page 92 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Empirical Bayes with Sequential Components by : Rohana Jith Karunamuni
Download or read book Empirical Bayes with Sequential Components written by Rohana Jith Karunamuni and published by . This book was released on 1985 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Parametric Empirical Bayes Problems with Cost for Component Observations by : Inna Jung
Download or read book Parametric Empirical Bayes Problems with Cost for Component Observations written by Inna Jung and published by . This book was released on 1988 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis On the asymptotic behaviour of monotonized empirical bayes rule... by : Theo Stijnen
Download or read book On the asymptotic behaviour of monotonized empirical bayes rule... written by Theo Stijnen and published by . This book was released on 19?? with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis The Multiple Monotone Empirical Bayes Rules with Floating Optimal Sample Size by : Pekka Laippala
Download or read book The Multiple Monotone Empirical Bayes Rules with Floating Optimal Sample Size written by Pekka Laippala and published by . This book was released on 1982 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bulletin - Institute of Mathematical Statistics by : Institute of Mathematical Statistics
Download or read book Bulletin - Institute of Mathematical Statistics written by Institute of Mathematical Statistics and published by . This book was released on 1992 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Statistics Subject Indexes from Mathematical Reviews by : American Mathematical Society
Download or read book Statistics Subject Indexes from Mathematical Reviews written by American Mathematical Society and published by . This book was released on 1987 with total page 540 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Mathematical Reviews by : American Mathematical Society
Download or read book Mathematical Reviews written by American Mathematical Society and published by American Mathematical Society(RI). This book was released on 1986-12 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Current Index to Statistics, Applications, Methods and Theory by :
Download or read book Current Index to Statistics, Applications, Methods and Theory written by and published by . This book was released on 1985 with total page 878 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Current Index to Statistics (CIS) is a bibliographic index of publications in statistics, probability, and related fields.
Book Synopsis Convergence Rates for Empirical Bayes Two-action Problems II, Continuous Case by : STANFORD UNIV CALIF DEPT OF STATISTICS.
Download or read book Convergence Rates for Empirical Bayes Two-action Problems II, Continuous Case written by STANFORD UNIV CALIF DEPT OF STATISTICS. and published by . This book was released on 1967 with total page 28 pages. Available in PDF, EPUB and Kindle. Book excerpt: A sequence of decision problems is considered where for each problem the observation has a probability density function of exponential type with parameter lambda where lambda is selected independently for each problem according to an unknown prior distribution G(lambda). It is supposed that in each of the problems, one of two possible actions (e.g., 'accept' or 'reject') must be taken. Under various assumptions, reasonably sharp upper bounds are found for the rate at which the risk of the nth problem approaches the smallest possible risk for certain refinements of the standard empirical Bayes procedures. For suitably chosen procedures, under situations likely to occur in practice, rates faster than n to the power ( -1 + epsilon) may be obtained for arbitrarily small epsilon> 0. Arbitrarily slow rates can occur in pathological situations. (Author).
Download or read book Core Statistics written by Simon N. Wood and published by Cambridge University Press. This book was released on 2015-04-13 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Core Statistics is a compact starter course on the theory, models, and computational tools needed to make informed use of powerful statistical methods.
Book Synopsis Doing Bayesian Data Analysis by : John Kruschke
Download or read book Doing Bayesian Data Analysis written by John Kruschke and published by Academic Press. This book was released on 2010-11-25 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt: There is an explosion of interest in Bayesian statistics, primarily because recently created computational methods have finally made Bayesian analysis tractable and accessible to a wide audience. Doing Bayesian Data Analysis, A Tutorial Introduction with R and BUGS, is for first year graduate students or advanced undergraduates and provides an accessible approach, as all mathematics is explained intuitively and with concrete examples. It assumes only algebra and ‘rusty’ calculus. Unlike other textbooks, this book begins with the basics, including essential concepts of probability and random sampling. The book gradually climbs all the way to advanced hierarchical modeling methods for realistic data. The text provides complete examples with the R programming language and BUGS software (both freeware), and begins with basic programming examples, working up gradually to complete programs for complex analyses and presentation graphics. These templates can be easily adapted for a large variety of students and their own research needs.The textbook bridges the students from their undergraduate training into modern Bayesian methods. Accessible, including the basics of essential concepts of probability and random sampling Examples with R programming language and BUGS software Comprehensive coverage of all scenarios addressed by non-bayesian textbooks- t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis). Coverage of experiment planning R and BUGS computer programming code on website Exercises have explicit purposes and guidelines for accomplishment
Book Synopsis Introduction to Probability and Statistics Using R by : G. Jay Kerns
Download or read book Introduction to Probability and Statistics Using R written by G. Jay Kerns and published by Lulu.com. This book was released on 2010-01-10 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.
Book Synopsis Simulation and the Monte Carlo Method by : Reuven Y. Rubinstein
Download or read book Simulation and the Monte Carlo Method written by Reuven Y. Rubinstein and published by John Wiley & Sons. This book was released on 2016-10-21 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese
Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Book Synopsis Doing Bayesian Data Analysis by : John Kruschke
Download or read book Doing Bayesian Data Analysis written by John Kruschke and published by Academic Press. This book was released on 2014-11-11 with total page 772 pages. Available in PDF, EPUB and Kindle. Book excerpt: Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan, Second Edition provides an accessible approach for conducting Bayesian data analysis, as material is explained clearly with concrete examples. Included are step-by-step instructions on how to carry out Bayesian data analyses in the popular and free software R and WinBugs, as well as new programs in JAGS and Stan. The new programs are designed to be much easier to use than the scripts in the first edition. In particular, there are now compact high-level scripts that make it easy to run the programs on your own data sets. The book is divided into three parts and begins with the basics: models, probability, Bayes' rule, and the R programming language. The discussion then moves to the fundamentals applied to inferring a binomial probability, before concluding with chapters on the generalized linear model. Topics include metric-predicted variable on one or two groups; metric-predicted variable with one metric predictor; metric-predicted variable with multiple metric predictors; metric-predicted variable with one nominal predictor; and metric-predicted variable with multiple nominal predictors. The exercises found in the text have explicit purposes and guidelines for accomplishment. This book is intended for first-year graduate students or advanced undergraduates in statistics, data analysis, psychology, cognitive science, social sciences, clinical sciences, and consumer sciences in business. - Accessible, including the basics of essential concepts of probability and random sampling - Examples with R programming language and JAGS software - Comprehensive coverage of all scenarios addressed by non-Bayesian textbooks: t-tests, analysis of variance (ANOVA) and comparisons in ANOVA, multiple regression, and chi-square (contingency table analysis) - Coverage of experiment planning - R and JAGS computer programming code on website - Exercises have explicit purposes and guidelines for accomplishment - Provides step-by-step instructions on how to conduct Bayesian data analyses in the popular and free software R and WinBugs
Book Synopsis Bandit Algorithms by : Tor Lattimore
Download or read book Bandit Algorithms written by Tor Lattimore and published by Cambridge University Press. This book was released on 2020-07-16 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and rigorous introduction for graduate students and researchers, with applications in sequential decision-making problems.
