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Probability & Statistics - Bayesian Analysis

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The Oxford Handbook of Applied Bayesian Analysis
by O' Hagan, Anthony    West, Mike   
(Paperback - English - 9780198703174)
Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, a ..
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Special Order
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Data Analysis: A Bayesian Tutorial
by Sivia, Devinderjit    Skilling, John   
(Paperback - English - 9780198568322)
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. After explaining the basic principles of Bayesian probability theory, their use is illustrated with a variety of examples ranging from elementary parameter estimation to image processing. Other topics covered include reliability analysis, multivariate optimization, least-squares and maximum likelihood, error-propagati ..
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The Oxford Handbook of Applied Bayesian Analysis
by O'Hagan, Anthony    West, Mike   
(Hardcover - English - 9780199548903)
Bayesian analysis has developed rapidly in applications in the last two decades and research in Bayesian methods remains dynamic and fast-growing. Dramatic advances in modelling concepts and computational technologies now enable routine application of Bayesian analysis using increasingly realistic stochastic models, and this drives the adoption of Bayesian approaches in many areas of science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of application areas. Chapters written by leading exponents of applied Bayesian analysis showcase the scientific ease and natural application of Bayesian modelling, a ..
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Information Processing: Supplemental Exercises for Volume I
by Blower, David J.       
(Paperback - English - 9781981338139)
This book contains an extensive number of fully solved problems in Boolean Algebra, Classical Logic, Cellular Automata, and formal probability manipulations to augment the already existing exercises of Volume I.Thus, the problems solved here are somewhat harder or more involved than those appearing in Volume I.also indulge in the luxury of taking as much space asdesire to fully explicate some core concepts that could only be touched upon lightly in Volume I.For example,devote a full eight pages and four separate exercises to explain the combinatorial counting formulas through Feller's classic problem of computing the probability of the number of accidents during ..
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Martingale Methods in Statistics
by Nishiyama, Yoichi       
(Hardcover - English - 9781466582811)
strong Martingale Methods in Statistics strong provides a unique introduction to statistics of stochastic processes written with the author's strong desire to present what is not available in other textbooks. While the author chooses to omit the well-known proofs of some of fundamental theorems in martingale theory by making clear citations instead, the author does his best to describe some intuitive interpretations or concrete usages of such theorems. On the other hand, the exposition of relatively new theorems in asymptotic statistics is presented in a completely self-contained way. Some simple, easy-to-understand proofs of martingale central limit theorems ..
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Statistics for Astrophysics: Bayesian Methodology
by Fraix‐burnet, Didier    Girard, Stéphane    Arbel, Julyan
(Paperback - English - 9782759822744)
This book includes the lectures given during the third session of the School of Statistics for Astrophysics that took place at Autrans, near Grenoble, in France, in October 2017. The subject is Bayesian Methodology. The interest of this statistical approach in astrophysics probably comes from its necessity and its success in determining the cosmological parameters from observations, especially from the cosmic background fluctuations. The cosmological community has thus been very active in this field for many years. But the Bayesian methodology, complementary to the more classical frequentist one, has many applications in physics in general due to its ability to ..
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Introduction to Probability with Mathematica
by Hastings, Kevin J.       
(Paperback - English - 9780367385194)
Updated to conform to Mathematica (R) 7.0, strong Introduction strong strong to Probability with Mathematica (R), Second Edition strong continues to show students how to easily create simulations from templates and solve problems using Mathematica . It provides a real understanding of probabilistic modeling and the analysis of data and encourages the application of these ideas to practical problems. The accompanyingdownloadable resources offer instructors the option of creating class notes, demonstrations, and projects. strong New to the Second Edition strong ul li Expanded section on Markov chains that includes a study of absorbing chains li ..
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Bayesian Model Selection and Statistical Modeling
by Ando, Tomohiro       
(Paperback - English - 9780367383978)
Along with many practical applications, strong Bayesian Model Selection and Statistical Modeling strong presents an array of Bayesian inference and model selection procedures. It thoroughly explains the concepts, illustrates the derivations of various Bayesian model selection criteria through examples, and provides R code for implementation. The author shows how to implement a variety of Bayesian inference using R and sampling methods, such as Markov chain Monte Carlo. He covers the different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors, the Bayesian predictive information criterion, and t ..
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Information Processing: An Introduction to Information Geometry
by Blower, David J.       
(Paperback - English - 9781530194230)
This book attempts to lay down some minimal set of coherent and consistent requirements for Advanced Information Processors (AIPs). The idea is advanced that AIPs should reason in an optimal manner by generalizing logic. Logic is generalized through the auspices of probability theory. Probability is presented through the viewpoint of the formal manipulation rules such as Bayes's Theorem and the maximum entropy principle. Information Geometry is an alternative way to think about the mathematical characterization of entropy and the assignment of numerical values to probabilities. The approach emphasizes many solved numerical examples and relies upon the computatio ..
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Statistical Methods with Applications to Demography and Life Insurance
by Khmaladze, Estate V.       
(Paperback - English - 9780367380236)
Suitable for statisticians, mathematicians, actuaries, and students interested in the problems of insurance and analysis of lifetimes, strong Statistical Methods with Applications to Demography and Life Insurance strong presents contemporary statistical techniques for analyzing life distributions and life insurance problems. It not only contains traditional material but also incorporates new problems and techniques not discussed in existing actuarial literature. The book mainly focuses on the analysis of an individual life and describes statistical methods based on empirical and related processes. Coverage ranges from analyzing the tails of distributions of ..
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Statistical Data Fusion
by Kedem, Benjamin    Oliveira, Victor de    Sverchkov, Michael
(Hardcover - English - 9789813200180)
'The book provides a comprehensive review of the DRM approach to data fusion. It is well written and easy to follow, although the technical details are not trivial. The authors did an excellent job in making a concise introduction of the statistical techniques in data fusion. The book contains several real data ... Overall,found that the book covers an important topic and the DRM is a promising tool in this area. Researchers on data fusion will surely find this book very helpful andwill use this book in studying with my PhD students.'Journal of the American Statistical AssociationThis book comes up with estimates or decisions based on multiple data sources as op ..
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Essential Methods for Design Based Sample Surveys
by Pfeffermann, Danny    Rao, C. R.   
(Paperback - English - 9780444638267)
Essential Methods for Design Based Sample Surveys presents key method contributions selected from the volume in the Handbook of Statistics: Sample Surveys: Design, Methods and Applications, Vol. 29a (2009). This essential reference provides specific aspects of sample survey design, with references to important contributions and available software. The content is aimed at researchers and practitioners who use statistical methods in design based sample surveys and market research. This book presents the core essential methods of sample selection and data processing. The data processing discussion covers editing and imputation, and methods of disclosure control. ..
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Bayes' Rule: A Tutorial Introduction to Bayesian Analysis
by Stone, James V.       
(Hardcover - English - 9780993367915)
Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation using the MatLab and Python programs provided online. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of Bayesian analysis. ..
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Stochastic Processes: From Applications to Theory
by del Moral, Pierre    Penev, Spiridon   
(Hardcover - English - 9781498701839)
Unlike traditional books presenting stochastic processes in an academic way, this book includes concrete applications that students will find interesting such as gambling, finance, physics, signal processing, statistics, fractals, and biology. Written with an important illustrated guide in the beginning, it contains many illustrations, photos and pictures, along with several website links. Computational tools such as simulation and Monte Carlo methods are included as well as complete toolboxes for both traditional and new computational techniques. ..
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Bayesian Analysis for the Social Sciences
by Jackman, Simon       
(Hardcover - English - 9780470011546)
Bayesian methods are increasingly being used in the social sciences, as the problems encountered lend themselves so naturally to the subjective qualities of Bayesian methodology. This book provides an accessible introduction to Bayesian methods, tailored specifically for social science students. It contains lots of real examples from political science, psychology, sociology, and economics, exercises in all chapters, and detailed descriptions of all the key concepts, without assuming any background in statistics beyond a first course. It features examples of how to implement the methods using WinBUGS - the most-widely used Bayesian analysis software in the world ..
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Probability and Statistics with R
by Ugarte, Maria Dolores    Militino, Ana F.    Arnholt, Alan T.
(Hardcover - English - 9781466504394)
Cohesively Incorporates Statistical Theory with R Implementation Since the publication of the popular first edition of this comprehensive textbook, the contributed R packages on CRAN have increased from around 1,000 to over 6,000. Designed for an intermediate undergraduate course, strong Probability and Statistics with R, Second Edition strong explores how some of these new packages make analysis easier and more intuitive as well as create more visually pleasing graphs. New to the Second Edition ul li Improvements to existing examples, problems, concepts, data, and functions li li New examples and exercises that use the most modern functions li li ..
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Bayesian Inference: Data Evaluation and Decisions
by Harney, Hanns Ludwig       
(Hardcover - English - 9783319416427)
Knowledge an Logic.- Bayes' Theorem.- Probable and Improbable Data.- Descriptions of Distributions I: Real x.- Description of Distributions II: Natural x .- Form Invariance I.- Examples of Invariant Measures.- A Linear Representation of Form Invariance.- Going Beyond Form Invariance: The Geometric Prior.- Inferring the Mean or Standard Deviation.- Form Invariance II: Natural x .- Item Response Theory.- On the Art of Fitting .- Problems and Solutions.- Description of Distributions I.- Real x.- Form Invariance I.- Beyond Form Invariance: The Geometric Prior.- Inferring Mean or Standard Deviation.- Form Invariance II: Natural x .- Item Response Theory.- On the Art ..
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Bayes Theorem: The Ultimate Beginner's Guide to Bayes Theorem
by Taff, Arthur       
(Paperback - English - 9781984178947)
The Perfect Book for Beginners Wanting to Visually Learn About Bayes Theorem Through Real Examples What if you could quickly and easily learn Bayesian data analysis without complex textbooks and statistics classes?Imagine being able to apply your newly learned theory to real life situations Multi-time best selling IT & mathematics author , Arthur Taff, presents the perfect guide for any beginner. Bayesian data analysis can be difficult to learn, especially through textbooks and statistic classes at school. This book aims to solve that issue by presenting the theories in an easy-to-understand and visually intuitive way. This book contains a number of visual exam ..
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Restocking
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Stochastic Population Processes: Analysis, Approximations, Simulations
by Renshaw, Eric       
(Paperback - English - 9780198739067)
The vast majority of random processes in the real world have no memory - the next step in their development depends purely on their current state. Stochastic realizations are therefore defined purely in terms of successive event-time pairs, and such systems are easy to simulate irrespective of their degree of complexity. However, whilst the associated probability equations are straightforward to write down, their solution usually requires the use of approximation and perturbation procedures. Traditional books, heavy in mathematical theory, often ignore such methods and attempt to force problems into a rigid framework of closed-form solutions. This text, strongl ..
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Special Order
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Bayes' Rule With R: A Tutorial Introduction to Bayesian Analysis
by Stone, James V.       
(Paperback - English - 9780993367946)
Discovered by an 18th century mathematician and preacher, Bayes' rule is a cornerstone of modern probability theory. In this richly illustrated book, a range of accessible examples is used to show how Bayes' rule is actually a natural consequence of common sense reasoning. Bayes' rule is then derived using intuitive graphical representations of probability, and Bayesian analysis is applied to parameter estimation. The tutorial style of writing, combined with a comprehensive glossary, makes this an ideal primer for novices who wish to become familiar with the basic principles of Bayesian analysis. Note that this book includes R (3.2) code snippets, which reproduc ..
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