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Author : Moritz von Rohrscheidt
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (11 download)
Download or read book Bayesian Nonparametric Inference for Queueing Systems written by Moritz von Rohrscheidt and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Damon Disch
Publisher :
ISBN 13 :
Total Pages : 236 pages
Book Rating : 4.:/5 (442 download)
Download or read book Bayesian Nonparametric Inference for Quantiles written by Damon Disch and published by . This book was released on 1978 with total page 236 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 : Purushottam Laud
Publisher :
ISBN 13 :
Total Pages : 216 pages
Book Rating : 4.:/5 (395 download)
Download or read book Bayesian Nonparametric Inference in Reliability Theory written by Purushottam Laud and published by . This book was released on 1977 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 : Stephen Walker
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (486 download)
Download or read book Bayesian Nonparametric Inference for Random Distributions and Related Functions written by Stephen Walker and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Michael Joseph Daniels
Publisher :
ISBN 13 : 9780429324222
Total Pages : 0 pages
Book Rating : 4.3/5 (242 download)
Download or read book Bayesian Nonparametrics for Causal Inference and Missing Data written by Michael Joseph Daniels and published by . This book was released on 2023 with total page 0 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 : Subhashis Ghosal
Publisher :
ISBN 13 :
Total Pages : 656 pages
Book Rating : 4.:/5 (113 download)
Download or read book Fundamentals of Nonparametric Bayesian Inference written by Subhashis Ghosal and published by . This book was released on 2017 with total page 656 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical advances of the past twenty years to synthesize all aspects of Bayesian nonparametrics, from prior construction to computation and large sample behavior of posteriors. Because understanding the behavior of posteriors is critical to selecting priors that work, the large sample theory is developed systematically, illustrated by various examples of model and prior combinations. Precise sufficient conditions are given, with complete proofs, that ensure desirable posterior properties and behavior. Each chapter ends with historical notes and numerous exercises to deepen and consolidate the reader's understanding, making the book valuable for both graduate students and researchers in statistics and machine learning, as well as in application areas such as econometrics and biostatistics.
Author : Deepthi V
Publisher : A.K. Publications
ISBN 13 : 9788400302351
Total Pages : 0 pages
Book Rating : 4.3/5 (23 download)
Download or read book Bayesian Inference on the Steady State Characteristics of Some Advanced Queueing Models written by Deepthi V and published by A.K. Publications. This book was released on 2022-12-22 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Queuing theory is the mathematical study of queuing, or waiting in lines. Queues contain customers such as people, objects, or information. Queues form when there are limited resources for providing a service. A basic queuing system consists of an arrival process (how customers arrive at the queue, how many customers are present in total), the queue itself, the service process for attending to those customers, and departures from the system. Essentials in modern life would not be possible without queueing theory. The purpose of this thesis is to address the inferential problems associated with various single/multi-server queueing models. It is mainly focused on the estimation of queue parameters like arrival rate, service rate and some important steady state queue characteristics such as traffic intensity, expected queue size, expected system size and expected waiting time. The study of queueing model is basically motivated by its use in communication system and computer networks. The development of an appropriate stochastic models is one of the major problem associated with the study of communication systems as it requires more and more sophistication to manage their complexity. Queueing theory was developed to provide models to predict the behavior of the systems that attempt to provide service for randomly arising demand. The earliest problems studied were those of telephone traffic congestion. The pioneer investigator was the Danish mathematician, A. K. Erlang, who, in 1909, published "The theory of Probabilities and Telephone Conversations". In later works he observed that a telephone system was generally characterized by either Poisson input, exponential service times, and multiple servers, or Poisson input, constant service times, and a single channel. There are many valuable applications of the theory, most of which have been well documented in the literature of probability, operations research, management science, and industrial engineering. Some examples are traffic flow (vehicles, aircraft, people, communications), scheduling (patients in hospitals, jobs on machines, programs on a computer), and facility design (bank, post offices, amusement parks, fast-food restaurants).
Author :
Publisher :
ISBN 13 :
Total Pages : 240 pages
Book Rating : 4.:/5 (94 download)
Download or read book Some Contributions to Bayesian Nonparametric Inference written by and published by . This book was released on 1994 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Paul Damien
Publisher :
ISBN 13 :
Total Pages : 20 pages
Book Rating : 4.:/5 (316 download)
Download or read book Implementation of Bayesian Nonparametric Inference Based on Beta Processes written by Paul Damien and published by . This book was released on 1994 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Xiaoguang Xu
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (16 download)
Download or read book Bayesian Nonparametric Inference for Stochastic Epidemic Models written by Xiaoguang Xu and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :
Publisher :
ISBN 13 :
Total Pages : 14 pages
Book Rating : 4.:/5 (933 download)
Download or read book On Extending Bayesian Nonparametric Inference to Stationary Data written by and published by . This book was released on 2003 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : U. Narayan Bhat
Publisher : Birkhäuser
ISBN 13 : 0817684212
Total Pages : 343 pages
Book Rating : 4.8/5 (176 download)
Download or read book An Introduction to Queueing Theory written by U. Narayan Bhat and published by Birkhäuser. This book was released on 2015-07-09 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: This introductory textbook is designed for a one-semester course on queueing theory that does not require a course on stochastic processes as a prerequisite. By integrating the necessary background on stochastic processes with the analysis of models, the work provides a sound foundational introduction to the modeling and analysis of queueing systems for a broad interdisciplinary audience of students in mathematics, statistics, and applied disciplines such as computer science, operations research, and engineering. This edition includes additional topics in methodology and applications. Key features: • An introductory chapter including a historical account of the growth of queueing theory in more than 100 years. • A modeling-based approach with emphasis on identification of models • Rigorous treatment of the foundations of basic models commonly used in applications with appropriate references for advanced topics. • A chapter on matrix-analytic method as an alternative to the traditional methods of analysis of queueing systems. • A comprehensive treatment of statistical inference for queueing systems. • Modeling exercises and review exercises when appropriate. The second edition of An Introduction of Queueing Theory may be used as a textbook by first-year graduate students in fields such as computer science, operations research, industrial and systems engineering, as well as related fields such as manufacturing and communications engineering. Upper-level undergraduate students in mathematics, statistics, and engineering may also use the book in an introductory course on queueing theory. With its rigorous coverage of basic material and extensive bibliography of the queueing literature, the work may also be useful to applied scientists and practitioners as a self-study reference for applications and further research. "...This book has brought a freshness and novelty as it deals mainly with modeling and analysis in applications as well as with statistical inference for queueing problems. With his 40 years of valuable experience in teaching and high level research in this subject area, Professor Bhat has been able to achieve what he aimed: to make [the work] somewhat different in content and approach from other books." - Assam Statistical Review of the first edition
Author : Albert Yee-Lap Lo
Publisher :
ISBN 13 :
Total Pages : 140 pages
Book Rating : 4.:/5 (669 download)
Download or read book Some Contributions to Bayesian Nonparametric Statistical Inference written by Albert Yee-Lap Lo and published by . This book was released on 1978 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 : David Insua
Publisher : John Wiley & Sons
ISBN 13 : 1118304039
Total Pages : 315 pages
Book Rating : 4.1/5 (183 download)
Download or read book Bayesian Analysis of Stochastic Process Models written by David Insua and published by John Wiley & Sons. This book was released on 2012-04-02 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian analysis of complex models based on stochastic processes has in recent years become a growing area. This book provides a unified treatment of Bayesian analysis of models based on stochastic processes, covering the main classes of stochastic processing including modeling, computational, inference, forecasting, decision making and important applied models. Key features: Explores Bayesian analysis of models based on stochastic processes, providing a unified treatment. Provides a thorough introduction for research students. Computational tools to deal with complex problems are illustrated along with real life case studies Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research (OR), engineering, finance, economics, computer science and Bayesian analysis will benefit from reading this book. With numerous applications included, practitioners of OR, stochastic modelling and applied statistics will also find this book useful.
