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Author : Nan Cheng
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (756 download)
Download or read book Bayesian Nonparametric Reliability Analysis Using Dirichlet Process Mixture Model written by Nan Cheng and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Peter Müller
Publisher : Springer
ISBN 13 : 3319189689
Total Pages : 203 pages
Book Rating : 4.3/5 (191 download)
Download or read book Bayesian Nonparametric Data Analysis written by Peter Müller and published by Springer. This book was released on 2015-06-17 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews nonparametric Bayesian methods and models that have proven useful in the context of data analysis. Rather than providing an encyclopedic review of probability models, the book’s structure follows a data analysis perspective. As such, the chapters are organized by traditional data analysis problems. In selecting specific nonparametric models, simpler and more traditional models are favored over specialized ones. The discussed methods are illustrated with a wealth of examples, including applications ranging from stylized examples to case studies from recent literature. The book also includes an extensive discussion of computational methods and details on their implementation. R code for many examples is included in online software pages.
Author : Chris Tsokos
Publisher : Elsevier
ISBN 13 : 032314585X
Total Pages : 566 pages
Book Rating : 4.3/5 (231 download)
Download or read book The Theory and Applications of Reliability With Emphasis on Bayesian and Nonparametric Methods written by Chris Tsokos and published by Elsevier. This book was released on 2012-12-02 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Theory and Applications of Reliability: With Emphasis on Bayesian and Nonparametric Methods, Volume I covers the proceedings of the conference on ""The Theory and Applications of Reliability with Emphasis on Bayesian and Nonparametric Methods."" The conference is organized so as to have technical presentations, a clinical session, and round table discussions. This volume is a 29-chapter text that specifically deals with the theoretical aspects of reliability estimation. Considerable chapters on the technical sessions are devoted to initial findings on the theory and applications of reliability estimation, with special emphasis on Bayesian and nonparametric methods. A Bayesian analysis implies the use of suitable prior information in association with Bayes theorem while the nonparametric approach analyzes the reliability components and systems under the assumption of a time-to-failure distribution with a wide defining property rather than a specific parametric class of probability distributions. The clinical session chapters discuss the actual problems encountered in reliability estimation. The remaining chapters deal with the status of the subject areas and the empirical Bayes developments. These chapters also present various probabilistic and statistic methods for reliability estimation. Theoreticians and reliability engineers will find this book invaluable.
Author : Nozer D. Singpurwalla
Publisher : John Wiley & Sons
ISBN 13 : 0470060336
Total Pages : 396 pages
Book Rating : 4.4/5 (7 download)
Download or read book Reliability and Risk written by Nozer D. Singpurwalla and published by John Wiley & Sons. This book was released on 2006-08-14 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: We all like to know how reliable and how risky certain situations are, and our increasing reliance on technology has led to the need for more precise assessments than ever before. Such precision has resulted in efforts both to sharpen the notions of risk and reliability, and to quantify them. Quantification is required for normative decision-making, especially decisions pertaining to our safety and wellbeing. Increasingly in recent years Bayesian methods have become key to such quantifications. Reliability and Risk provides a comprehensive overview of the mathematical and statistical aspects of risk and reliability analysis, from a Bayesian perspective. This book sets out to change the way in which we think about reliability and survival analysis by casting them in the broader context of decision-making. This is achieved by: Providing a broad coverage of the diverse aspects of reliability, including: multivariate failure models, dynamic reliability, event history analysis, non-parametric Bayes, competing risks, co-operative and competing systems, and signature analysis. Covering the essentials of Bayesian statistics and exchangeability, enabling readers who are unfamiliar with Bayesian inference to benefit from the book. Introducing the notion of “composite reliability”, or the collective reliability of a population of items. Discussing the relationship between notions of reliability and survival analysis and econometrics and financial risk. Reliability and Risk can most profitably be used by practitioners and research workers in reliability and survivability as a source of information, reference, and open problems. It can also form the basis of a graduate level course in reliability and risk analysis for students in statistics, biostatistics, engineering (industrial, nuclear, systems), operations research, and other mathematically oriented scientists, wherein the instructor could supplement the material with examples and problems.
Author : Athanasios Kottas
Publisher :
ISBN 13 :
Total Pages : 294 pages
Book Rating : 4.:/5 (452 download)
Download or read book Bayesian Nonparametric and Semiparametric Modeling Using Dirichlet Process Mixing written by Athanasios Kottas and published by . This book was released on 2000 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michael S. Hamada
Publisher : Springer Science & Business Media
ISBN 13 : 0387779507
Total Pages : 445 pages
Book Rating : 4.3/5 (877 download)
Download or read book Bayesian Reliability written by Michael S. Hamada and published by Springer Science & Business Media. This book was released on 2008-08-15 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Reliability presents modern methods and techniques for analyzing reliability data from a Bayesian perspective. The adoption and application of Bayesian methods in virtually all branches of science and engineering have significantly increased over the past few decades. This increase is largely due to advances in simulation-based computational tools for implementing Bayesian methods. The authors extensively use such tools throughout this book, focusing on assessing the reliability of components and systems with particular attention to hierarchical models and models incorporating explanatory variables. Such models include failure time regression models, accelerated testing models, and degradation models. The authors pay special attention to Bayesian goodness-of-fit testing, model validation, reliability test design, and assurance test planning. Throughout the book, the authors use Markov chain Monte Carlo (MCMC) algorithms for implementing Bayesian analyses -- algorithms that make the Bayesian approach to reliability computationally feasible and conceptually straightforward. This book is primarily a reference collection of modern Bayesian methods in reliability for use by reliability practitioners. There are more than 70 illustrative examples, most of which utilize real-world data. This book can also be used as a textbook for a course in reliability and contains more than 160 exercises. Noteworthy highlights of the book include Bayesian approaches for the following: Goodness-of-fit and model selection methods Hierarchical models for reliability estimation Fault tree analysis methodology that supports data acquisition at all levels in the tree Bayesian networks in reliability analysis Analysis of failure count and failure time data collected from repairable systems, and the assessment of various related performance criteria Analysis of nondestructive and destructive degradation data Optimal design of reliability experiments Hierarchical reliability assurance testing
Author : Tatiana V Tatarinova
Publisher : World Scientific
ISBN 13 : 1783266279
Total Pages : 296 pages
Book Rating : 4.7/5 (832 download)
Download or read book Nonlinear Mixture Models: A Bayesian Approach written by Tatiana V Tatarinova and published by World Scientific. This book was released on 2014-12-30 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book, written by two mathematicians from the University of Southern California, provides a broad introduction to the important subject of nonlinear mixture models from a Bayesian perspective. It contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications. It is self-contained and unified in presentation, which makes it ideal for use as an advanced textbook by graduate students and as a reference for independent researchers. The explanations in the book are detailed enough to capture the interest of the curious reader, and complete enough to provide the necessary background material needed to go further into the subject and explore the research literature.In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown, number of components. These methods are then applied to various problems including population pharmacokinetics and gene expression analysis. In population pharmacokinetics, the nonlinear mixture model, based on previous clinical data, becomes the prior distribution for individual therapy. For gene expression data, one application included in the book is to determine which genes should be associated with the same component of the mixture (also known as a clustering problem). The book also contains examples of computer programs written in BUGS. This is the first book of its kind to cover many of the topics in this field.
Author : Michael J. Daniels
Publisher : CRC Press
ISBN 13 : 1000927717
Total Pages : 263 pages
Book Rating : 4.0/5 (9 download)
Download or read book Bayesian Nonparametrics for Causal Inference and Missing Data written by Michael J. Daniels and published by CRC Press. This book was released on 2023-08-23 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional relationships, and their interplay with causal inference and missing data. This book emphasizes the importance of making untestable assumptions to identify estimands of interest, such as missing at random assumption for missing data and unconfoundedness for causal inference in observational studies. Unlike parametric methods, the BNP approach can account for possible violations of assumptions and minimize concerns about model misspecification. The overall strategy is to first specify BNP models for observed data and then to specify additional uncheckable assumptions to identify estimands of interest. The book is divided into three parts. Part I develops the key concepts in causal inference and missing data and reviews relevant concepts in Bayesian inference. Part II introduces the fundamental BNP tools required to address causal inference and missing data problems. Part III shows how the BNP approach can be applied in a variety of case studies. The datasets in the case studies come from electronic health records data, survey data, cohort studies, and randomized clinical trials. Features • Thorough discussion of both BNP and its interplay with causal inference and missing data • How to use BNP and g-computation for causal inference and non-ignorable missingness • How to derive and calibrate sensitivity parameters to assess sensitivity to deviations from uncheckable causal and/or missingness assumptions • Detailed case studies illustrating the application of BNP methods to causal inference and missing data • R code and/or packages to implement BNP in causal inference and missing data problems The book is primarily aimed at researchers and graduate students from statistics and biostatistics. It will also serve as a useful practical reference for mathematically sophisticated epidemiologists and medical researchers.
Author : Matthew Alan Taddy
Publisher :
ISBN 13 :
Total Pages : 354 pages
Book Rating : 4.:/5 (79 download)
Download or read book Bayesian Nonparametric Analysis of Conditional Distributions and Inference for Poisson Point Processes written by Matthew Alan Taddy and published by . This book was released on 2008 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Marian Farah
Publisher :
ISBN 13 :
Total Pages : 296 pages
Book Rating : 4.:/5 (26 download)
Download or read book Bayesian Nonparametric Methods for Emulation, Sensitivity Analysis, and Calibration of Computer Simulators written by Marian Farah and published by . This book was released on 2011 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Milovan Krnjajić
Publisher :
ISBN 13 :
Total Pages : 256 pages
Book Rating : 4.:/5 (71 download)
Download or read book Contributions to Bayesian Statistical Analysis written by Milovan Krnjajić and published by . This book was released on 2005 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : stephen walker
Publisher :
ISBN 13 :
Total Pages : 50 pages
Book Rating : 4.L/5 ( download)
Download or read book bayesian nonparametric inference written by stephen walker and published by . This book was released on 1997 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Liu, Xi
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (988 download)
Download or read book Semi-parametric Bayesian Inference of Accelerated Life Test Using Dirichlet Process Mixture Model written by Liu, Xi and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Abel Rodriguez
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783838300122
Total Pages : 168 pages
Book Rating : 4.3/5 (1 download)
Download or read book Some Advances in Bayesian Nonparametric Modeling written by Abel Rodriguez and published by LAP Lambert Academic Publishing. This book was released on 2009-03 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametric and semiparametric mixture models have become extremely popular in the last 10 years because they provide flexibility and interpretability while preserving computational simplicity. This book is a contribution to this growing literature, discussing the design of models for collections of distributions and their application to density estimation and nonparametric regression. All methods introduced in this book are discussed in the context of complex scientific applications in public health, epidemiology and finance.
Author : Nils Lid Hjort
Publisher : Cambridge University Press
ISBN 13 : 1139484605
Total Pages : 309 pages
Book Rating : 4.1/5 (394 download)
Download or read book Bayesian Nonparametrics written by Nils Lid Hjort and published by Cambridge University Press. This book was released on 2010-04-12 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Author : Subhashis Ghosal
Publisher : Cambridge University Press
ISBN 13 : 0521878268
Total Pages : 671 pages
Book Rating : 4.5/5 (218 download)
Download or read book Fundamentals of Nonparametric Bayesian Inference written by Subhashis Ghosal and published by Cambridge University Press. This book was released on 2017-06-26 with total page 671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
Author : J.K. Ghosh
Publisher : Springer Science & Business Media
ISBN 13 : 0387226540
Total Pages : 311 pages
Book Rating : 4.3/5 (872 download)
Download or read book Bayesian Nonparametrics written by J.K. Ghosh and published by Springer Science & Business Media. This book was released on 2006-05-11 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first systematic treatment of Bayesian nonparametric methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
