Download or read book A Bayesian Approach to Sequential Sampling written by Jack Allen Klasky and published by . This book was released on 1966 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book A Bayesian Procedure for the Design of Sequential Sampling Plans written by Sidney Gordon Gilbreath and published by . This book was released on 1967 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Intervention Research written by Mark W. Fraser and published by Oxford University Press. This book was released on 2009-04-02 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: When social workers draw on experience, theory, or data in order to develop new strategies or enhance existing ones, they are conducting intervention research. This relatively new field involves program design, implementation, and evaluation and requires a theory-based, systematic approach. Intervention Research presents such a framework. The five-step strategy described in this brief but thorough book ushers the reader from an idea's germination through the process of writing a treatment manual, assessing program efficacy and effectiveness, and disseminating findings. Rich with examples drawn from child welfare, school-based prevention, medicine, and juvenile justice, Intervention Research relates each step of the process to current social work practice. It also explains how to adapt interventions for new contexts, and provides extensive examples of intervention research in fields such as child welfare, school-based prevention, medicine, and juvenile justice, and offers insights about changes and challenges in the field. This innovative pocket guide will serve as a solid reference for those already in the field, as well as help the next generation of social workers develop skills to contribute to the evolving field of intervention research.
Download or read book Bayesian Statistics A Review written by D. V. Lindley and published by SIAM. This book was released on 1972-01-31 with total page 100 pages. Available in PDF, EPUB and Kindle. Book excerpt: A study of those statistical ideas that use a probability distribution over parameter space. The first part describes the axiomatic basis in the concept of coherence and the implications of this for sampling theory statistics. The second part discusses the use of Bayesian ideas in many branches of statistics.
Download or read book A Bayesian Approach to Optimal Sequential Design for Destructive Life Testing written by Ping Shi and published by . This book was released on 1995 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sample size optimal Bayesian Schemes in Sequential Sampling written by Jonathan Biele and published by . This book was released on 1990 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Methods for Finite Population Sampling written by Malay Ghosh and published by CRC Press. This book was released on 1997-06-01 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assuming a basic knowledge of the frequentist approach to finite population sampling, Bayesian Methods for Finite Population Sampling describes Bayesian and predictive approaches to inferential problems with an emphasis on the likelihood principle. The authors demonstrate that a variety of levels of prior information can be used in survey sampling in a Bayesian manner. Situations considered range from a noninformative Bayesian justification of standard frequentist methods when the only prior information available is the belief in the exchangeability of the units to a full-fledged Bayesian model. Intended primarily for graduate students and researchers in finite population sampling, this book will also be of interest to statisticians who use sampling and lecturers and researchers in general statistics and biostatistics.
Download or read book Bayesian Theory and Applications written by Paul Damien and published by Oxford University Press. This book was released on 2013-01-24 with total page 717 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field.
Download or read book Bayesian group sequential sampling with application to tax auditing written by Ruth Etzioni and published by . This book was released on 1990 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Sequential Monte Carlo Methods in Practice written by Arnaud Doucet and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 590 pages. Available in PDF, EPUB and Kindle. Book excerpt: Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.
Download or read book Bayesian Methods for Finite Population Sampling written by Malay Ghosh and published by Routledge. This book was released on 2021-12-17 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assuming a basic knowledge of the frequentist approach to finite population sampling, Bayesian Methods for Finite Population Sampling describes Bayesian and predictive approaches to inferential problems with an emphasis on the likelihood principle. The authors demonstrate that a variety of levels of prior information can be used in survey sampling in a Bayesian manner. Situations considered range from a noninformative Bayesian justification of standard frequentist methods when the only prior information available is the belief in the exchangeability of the units to a full-fledged Bayesian model. Intended primarily for graduate students and researchers in finite population sampling, this book will also be of interest to statisticians who use sampling and lecturers and researchers in general statistics and biostatistics.
Download or read book Optimal Sequentially Planned Decision Procedures written by Norbert Schmitz and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learning from experience, making decisions on the basis of the available information, and proceeding step by step to a desired goal are fundamental behavioural qualities of human beings. Nevertheless, it was not until the early 1940's that such a statistical theory - namely Sequential Analysis - was created, which allows us to investigate this kind of behaviour in a precise manner. A. Wald's famous sequential probability ratio test (SPRT; see example (1.8» turned out to have an enormous influence on the development of this theory. On the one hand, Wald's fundamental monograph "Sequential Analysis" ([Wa]*) is essentially centered around this test. On the other hand, important properties of the SPRT - e.g. Bayes optimality, minimax-properties, "uniform" optimality with respect to expected sample sizes - gave rise to the development of a general statistical decision theory. As a conse quence, the SPRT's played a dominating role in the further development of sequential analysis and, more generally, in theoretical statistics.
Download or read book Bayesian Methods in Reliability written by P. Sander and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 227 pages. Available in PDF, EPUB and Kindle. Book excerpt: When data is collected on failure or survival a list of times is obtained. Some of the times are failure times and others are the times at which the subject left the experiment. These times both give information about the performance of the system. The two types will be referred to as failure and censoring times (cf. Smith section 5). * A censoring time, t, gives less information than a failure time, for it is * known only that the item survived past t and not when it failed. The data is tn and of censoring thus collected as a list of failure times t , . . . , l * * * times t , t , . . . , t • 1 z m 2. 2. Classical methods The failure times are assumed to follow a parametric distribution F(t;B) with and reliability R(t;B). There are several methods of estimating density f(t;B) the parameter B based only on the data in the sample without any prior assumptions about B. The availability of powerful computers and software packages has made the method of maximum likelihood the most popular. Descriptions of most methods can be found in the book by Mann, Schafer and Singpurwalla (1974). In general the method of maximum likelihood is the most useful of the classical approaches. The likelihood approach is based on constructing the joint probability distrilmtion or density for a sample.
Download or read book A Bayesian Approach to Two stage Sampling written by William Arnold Ericson and published by . This book was released on 1976 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: In several previous papers, the author has shown that various standard sampling designs are optimal in a Bayesian sense under corresponding classes of prior distributions on the N-dimensional vector of unknown characteristics of the N elements of a finite population. In this manner a Bayesian interpretation of simple random sampling, stratified random sampling, and of various ratio and regression estimators have been given. In the present report this work is extended to two-stage balanced sampling. Additionally, a simple result on a representation of finitely exchangeable discrete random variables is given which gives a slight generalization of a seemingly little-known result of de Finetti. Also a general tie between Bayes posterior means and traditional WLSE's and BLUE's is obtained, generalizing previous results given by the author. (Author).
Download or read book Bayes Rules written by Alicia A. Johnson and published by CRC Press. This book was released on 2022-03-03 with total page 606 pages. Available in PDF, EPUB and Kindle. Book excerpt: Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.
Download or read book How and when to Perform Bayesian Acceptance Sampling written by Thomas W. Calvin and published by Asq Corporation. This book was released on 1990 with total page 32 pages. Available in PDF, EPUB and Kindle. Book excerpt: This how-to book gives a clear rationale for the use of Bayesian methods by comparing them with conventional acceptance sampling approaches.Contents:Introduction to Bayesian Acceptance Sampling Bayesian Acceptance Sampling Distributions Examples of Plan Section
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.
