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Introduction to Probability Models
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Sheldon M Ross
"Introduction to Probability Models, 8th Edition", continues to introduce and inspire readers to the art of applying probability theory to phenomena in fields such as engineering, computer science, management and actuarial science, the physical and social sciences, and operations research. Now revised and updated, this best-selling book retains ...
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Real Analysis and Probability
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R M Dudley
This classic textbook offers a clear exposition of modern probability theory and of the interplay between the properties of metric spaces and probability measures. The first half of the book gives an exposition of real analysis: basic set theory, general topology, measure theory, integration, an introduction to functional analysis in Banach and ...
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Data Analysis: A Bayesian Tutorial
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D S Sivia
Statistics lectures have been a source of much bewilderment and frustration for generations of students. This book attempts to remedy the situation by expounding a logical and unified approach to the whole subject of data analysis. This text is intended as a tutorial guide for senior undergraduates and research students in science and engineering. ...
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Bayesian Data Analysis, Second Edition
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Andrew Gelman, John B Carlin, Hal S Stern
Incorporating new and updated information, this second edition of the bestselling text in Bayesian data analysis continues to emphasize practice over theory, describing how to conceptualize, perform, and critique statistical analysis from a Bayesian perspective. Its world-class authors provide guidance on all aspects of Bayesian data analysis and ...
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Bayesian Statistics: An Introduction
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Peter M Lee
Bayesian Statistics is the school of thought that uses all information surrounding the likelihood of an event rather than just that collected experimentally. Among statisticians the Bayesian approach continues to gain adherents and this new edition of Peter Lee's well-established introduction maintains the clarity of exposition and use of examples ...
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Stochastic processes and filtering theory
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Andrew H. Jazwinski
This book presents a unified treatment of linear and nonlinear filtering theory for engineers, with sufficient emphasis on applications to enable the reader to use the theory. The need for this book is twofold. First, although linear estimation theory is relatively well known, it is largely scattered in the journal literature and has not been ...
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Bayesian Modeling Using Winbugs
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Ioannis Ntzoufras
A hands-on introduction to the principles of Bayesian modeling. Using "WinBUGS Bayesian Modeling Using WinBUGS" provides an easily accessible introduction to the use of WinBUGS programming techniques in a variety of Bayesian modeling settings. The author provides an accessible treatment of the topic, offering readers a smooth introduction to the ...
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Introductory Probability Theory
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Janos Galambos, Galambos J
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Bayesian Reliability
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Michael S Hamada, Alyson G Wilson, C Shane Reese
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 ...
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Conditional Measures and Applications
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M M Rao
The second edition of this authoritative text offers an in-depth treatment of all aspects of conditional expectations and probability measures and their structural analysis. However, what makes this the definitive reference on conditional measures is the author's keen ability to link theory with practical applications. The author covers ...
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Bayesian Statistical Modelling
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by
Peter Congdon
Bayesian methods combine the evidence from the data at hand with previous quantitative knowledge to analyse practical problems in a wide range of areas. The calculations were previously complex, but it is now possible to routinely apply Bayesian methods due to advances in computing technology and the use of new sampling methods for estimating ...
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An Introduction to Probability and Statistics Using Basic
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R H Groeneveld, Richard A Groeneveld
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Introduction to Stochastic Processes
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Gregory F Lawler
Emphasizing fundamental mathematical ideas rather than proofs, "Introduction to Stochastic Processes, Second Edition" provides quick access to important foundations of probability theory applicable to problems in many fields. Assuming that you have a reasonable level of computer literacy, the ability to write simple programs, and the access to ...
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Introduction to Probability and Statistics, Second Edition,
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by
Narayan C Giri
Beginning with the historical background of probability theory, this thoroughly revised text examines all important aspects of mathematical probability - including random variables, probability distributions, characteristic and generating functions, stochatic convergence, and limit theorems - and provides an introduction to various types of ...
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Queues
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D. R. Cox, Walter L. Smith
This is a classic book on Queues. First published in 1961 it is clearly and concisely introduces the theory of queueing systems and is still just as relevant today. The monograph is aimed at both students and operational research workers concerned with the practical investigations of queueing, although almost every statistician will find its ...
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Multistage Selection and Ranking Procedures: Second Order Asymptotics
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Nitis Mukhopadhyay, Mukhopadhyay Mukhopadhyay, Tumulesh K S Solanky
This useful volume provides a thorough synthesis of second-order asymptotics in multistage sampling methodologies for selection and ranking unifying available second-order results in general and applying them to a host of situations. It contains, in each chapter, helpful notes and overviews to facilitate comprehension, as well as complements and ...
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Handbook of Stochastic Analysis and Applications
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D Kannan (Editor), V Lakshmikantham (Editor), Kannan Kannan
An introduction to general theories of stochastic processes and modern martingale theory. The volume focuses on consistency, stability and contractivity under geometric invariance in numerical analysis, and discusses problems related to implementation, simulation, variable step size algorithms, and random number generation.
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Randomized Response: Theory and Techniques
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Arijit Chaudhuri, R Mukerjee
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Introduction to Random Chaos
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Jerzy Szulga, Szulga Szulga
Introduction to Random Chaos contains a wealth of information on this significant area, rooted in hypercontraction and harmonic analysis. Random chaos statistics extend the classical concept of empirical mean and variance. By focusing on the three models of Rademacher, Poisson, and Wiener chaos, this book shows how an iteration of a simple random ...
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Predictive Inference
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Seymour Geisser, Geisser Geisser
The author's research has been directed towards inference involving observables rather than parameters and in this book he brings together his views on predictive or observable inference and his reason for his preference. He feels that inference couched in terms of observables has the possibility of being validated. While the book discusses a ...
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Error and the Growth of Experimental Knowledge
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Deborah G Mayo
We may learn from our mistakes, but this work argues that, where experimental knowledge is concerned, we haven't begun to learn enough. It provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as a more robust framework for the epistemology of experiment. Deborah Mayo ...
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Multiscale Modeling: A Bayesian Perspective
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Marco A R Ferreira, Herbert K H Lee
A wide variety of processes occur on multiple scales, either naturally or as a consequence of measurement. This book contains methodology for the analysis of data that arise from such multiscale processes. The book brings together a number of recent developments and makes them accessible to a wider audience. Taking a Bayesian approach allows for ...
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Bayesian Inference: Parameter Estimation and Decisions
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Hanns L Harney
Filling a longstanding need in the physical sciences, Bayesian Inference offers the first basic introduction for advanced undergraduates and graduates in the physical sciences. This text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. This usually occurs in frontier ...
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Markov Chain Monte Carlo: Innovations and Applications
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W S Kendall, F Liang, J-S Wang
Markov Chain Monte Carlo (MCMC) originated in statistical physics, but has spilled over into various application areas, leading to a corresponding variety of techniques and methods. That variety stimulates new ideas and developments from many different places, and there is much to be gained from cross-fertilization. This book presents five ...
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Bayes Linear Statistics: Theory and Methods
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Michael Goldstein, David Wooff
Bayesian methods combine information available from data with any prior information available from expert knowledge. The Bayes linear approach follows this path, offering a quantitative structure for expressing beliefs, and systematic methods for adjusting these beliefs, given observational data. The methodology differs from the full Bayesian ...
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