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Bayesian Inference

Bayesian Inference Author Hanns Ludwig Harney
ISBN-10 9783662060063
Release 2013-03-14
Pages 263
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Solving a longstanding problem in the physical sciences, this text and reference generalizes Gaussian error intervals to situations in which the data follow distributions other than Gaussian. The text is written at introductory level, with many examples and exercises.



Data Analysis Techniques for Physical Scientists

Data Analysis Techniques for Physical Scientists Author Claude Pruneau
ISBN-10 9781108416788
Release 2017-09-30
Pages
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A comprehensive guide to data analysis techniques for the physical sciences including probability, statistics, data reconstruction, data correction and Monte Carlo methods. This book provides a valuable resource for advanced undergraduate and graduate students, as well as practitioners in the fields of experimental particle physics, nuclear physics and astrophysics.



Probability and Statistics in Experimental Physics

Probability and Statistics in Experimental Physics Author Byron P. Roe
ISBN-10 0387951636
Release 2001-06-21
Pages 252
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Second edition includes a new chapter on queuing problems and introduces a new method for dealing with experiments in which only a few events are observed.



American Book Publishing Record

American Book Publishing Record Author
ISBN-10 UOM:39015066043152
Release 2003
Pages
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American Book Publishing Record has been writing in one form or another for most of life. You can find so many inspiration from American Book Publishing Record also informative, and entertaining. Click DOWNLOAD or Read Online button to get full American Book Publishing Record book for free.



Statistical Decision Theory

Statistical Decision Theory Author F. Liese
ISBN-10 9780387731940
Release 2008-12-30
Pages 677
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For advanced graduate students, this book is a one-stop shop that presents the main ideas of decision theory in an organized, balanced, and mathematically rigorous manner, while observing statistical relevance. All of the major topics are introduced at an elementary level, then developed incrementally to higher levels. The book is self-contained as it provides full proofs, worked-out examples, and problems. The authors present a rigorous account of the concepts and a broad treatment of the major results of classical finite sample size decision theory and modern asymptotic decision theory. With its broad coverage of decision theory, this book fills the gap between standard graduate texts in mathematical statistics and advanced monographs on modern asymptotic theory.



Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning Author David Barber
ISBN-10 9780521518147
Release 2012-02-02
Pages 697
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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.



Bayesian Spectrum Analysis and Parameter Estimation

Bayesian Spectrum Analysis and Parameter Estimation Author G. Larry Bretthorst
ISBN-10 9781468493993
Release 2013-03-09
Pages 209
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This work is essentially an extensive revision of my Ph.D. dissertation, [1J. It 1S primarily a research document on the application of probability theory to the parameter estimation problem. The people who will be interested in this material are physicists, economists, and engineers who have to deal with data on a daily basis; consequently, we have included a great deal of introductory and tutorial material. Any person with the equivalent of the mathematics background required for the graduate level study of physics should be able to follow the material contained in this book, though not without eIfort. From the time the dissertation was written until now (approximately one year) our understanding of the parameter estimation problem has changed extensively. We have tried to incorporate what we have learned into this book. I am indebted to a number of people who have aided me in preparing this docu ment: Dr. C. Ray Smith, Steve Finney, Juana Sunchez, Matthew Self, and Dr. Pat Gibbons who acted as readers and editors. In addition, I must extend my deepest thanks to Dr. Joseph Ackerman for his support during the time this manuscript was being prepared.



Probability Theory

Probability Theory Author E. T. Jaynes
ISBN-10 9781139435161
Release 2003-04-10
Pages
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The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications. This book goes beyond the conventional mathematics of probability theory, viewing the subject in a wider context. New results are discussed, along with applications of probability theory to a wide variety of problems in physics, mathematics, economics, chemistry and biology. It contains many exercises and problems, and is suitable for use as a textbook on graduate level courses involving data analysis. The material is aimed at readers who are already familiar with applied mathematics at an advanced undergraduate level or higher. The book will be of interest to scientists working in any area where inference from incomplete information is necessary.



Bayesian Filtering and Smoothing

Bayesian Filtering and Smoothing Author Simo Särkkä
ISBN-10 9781107030657
Release 2013-09-05
Pages 254
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A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.



Statistics

Statistics Author R. J. Barlow
ISBN-10 9781118723234
Release 2013-06-05
Pages 224
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The Manchester Physics Series General Editors: D. J. Sandiford; F. Mandl; A. C. Phillips Department of Physics and Astronomy, University of Manchester Properties of Matter B. H. Flowers and E. Mendoza Optics Second Edition F. G. Smith and J. H. Thomson Statistical Physics Second Edition F. Mandl Electromagnetism Second Edition I. S. Grant and W. R. Phillips Statistics R. J. Barlow Solid State Physics Second Edition J. R. Hook and H. E. Hall Quantum Mechanics F. Mandl Particle Physics Second Edition B. R. Martin and G. Shaw The Physics of Stars Second Edition A.C. Phillips Computing for Scientists R. J. Barlow and A. R. Barnett Written by a physicist, Statistics is tailored to the needs of physical scientists, containing and explaining all they need to know. It concentrates on parameter estimation, especially the methods of Least Squares and Maximum Likelihood, but other techniques, such as hypothesis testing, Bayesian statistics and non-parametric methods are also included. Intended for reasonably numerate scientists it contains all the basic formulae, their derivations and applications, together with some more advanced ones. Statistics features: * Comprehensive coverage of the essential techniques physical scientists are likely to need. * A wealth of examples, and problems with their answers. * Flexible structure and organisation allows it to be used as a course text and a reference. * A review of the basics, so that little prior knowledge is required.



Practical Bayesian Inference

Practical Bayesian Inference Author Coryn A. L. Bailer-Jones
ISBN-10 9781108127677
Release 2017-04-27
Pages
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Science is fundamentally about learning from data, and doing so in the presence of uncertainty. This volume is an introduction to the major concepts of probability and statistics, and the computational tools for analysing and interpreting data. It describes the Bayesian approach, and explains how this can be used to fit and compare models in a range of problems. Topics covered include regression, parameter estimation, model assessment, and Monte Carlo methods, as well as widely used classical methods such as regularization and hypothesis testing. The emphasis throughout is on the principles, the unifying probabilistic approach, and showing how the methods can be implemented in practice. R code (with explanations) is included and is available online, so readers can reproduce the plots and results for themselves. Aimed primarily at undergraduate and graduate students, these techniques can be applied to a wide range of data analysis problems beyond the scope of this work.



Introduction to Statistics and Data Analysis

Introduction to Statistics and Data Analysis Author Roxy Peck
ISBN-10 9781305445963
Release 2015-01-01
Pages 844
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Roxy Peck, Chris Olsen, and Jay Devore's new edition uses real data and attention-grabbing examples to introduce students to the study of statistics and data analysis. Traditional in structure yet modern in approach, this text guides students through an intuition-based learning process that stresses interpretation and communication of statistical information. Simple notation--including frequent substitution of words for symbols--helps students grasp concepts and cement their comprehension. Hands-on activities and interactive applets allow students to practice statistics firsthand. INTRODUCTION TO STATISTICS AND DATA ANALYSIS includes updated coverage of most major technologies, as well as expanded coverage of probability. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.



Classification Parameter Estimation and State Estimation

Classification  Parameter Estimation and State Estimation Author Bangjun Lei
ISBN-10 9781119152453
Release 2017-03-17
Pages 480
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A practical introduction to intelligent computer vision theory, design, implementation, and technology The past decade has witnessed epic growth in image processing and intelligent computer vision technology. Advancements in machine learning methods—especially among adaboost varieties and particle filtering methods—have made machine learning in intelligent computer vision more accurate and reliable than ever before. The need for expert coverage of the state of the art in this burgeoning field has never been greater, and this book satisfies that need. Fully updated and extensively revised, this 2nd Edition of the popular guide provides designers, data analysts, researchers and advanced post-graduates with a fundamental yet wholly practical introduction to intelligent computer vision. The authors walk you through the basics of computer vision, past and present, and they explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real-world examples, they explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including: PRTools5 software for MATLAB—especially the latest representation and generalization software toolbox for PRTools5 Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods The latest techniques for classification and supervised learning, with an emphasis on Neural Network, Genetic State Estimation and other particle filter and AI state estimation methods All new coverage of the Adaboost and its implementation in PRTools5. A valuable working resource for professionals and an excellent introduction for advanced-level students, this 2nd Edition features a wealth of illustrative examples, ranging from basic techniques to advanced intelligent computer vision system implementations. Additional examples and tutorials, as well as a question and solution forum, can be found on a companion website.



Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning Author Christopher M. Bishop
ISBN-10 1493938436
Release 2016-08-23
Pages 738
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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.



Information Theory Inference and Learning Algorithms

Information Theory  Inference and Learning Algorithms Author David J. C. MacKay
ISBN-10 0521642981
Release 2003-09-25
Pages 628
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Fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.



A Comparison of the Bayesian and Frequentist Approaches to Estimation

A Comparison of the Bayesian and Frequentist Approaches to Estimation Author Francisco J. Samaniego
ISBN-10 1441959416
Release 2010-06-14
Pages 225
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The main theme of this monograph is “comparative statistical inference. ” While the topics covered have been carefully selected (they are, for example, restricted to pr- lems of statistical estimation), my aim is to provide ideas and examples which will assist a statistician, or a statistical practitioner, in comparing the performance one can expect from using either Bayesian or classical (aka, frequentist) solutions in - timation problems. Before investing the hours it will take to read this monograph, one might well want to know what sets it apart from other treatises on comparative inference. The two books that are closest to the present work are the well-known tomes by Barnett (1999) and Cox (2006). These books do indeed consider the c- ceptual and methodological differences between Bayesian and frequentist methods. What is largely absent from them, however, are answers to the question: “which - proach should one use in a given problem?” It is this latter issue that this monograph is intended to investigate. There are many books on Bayesian inference, including, for example, the widely used texts by Carlin and Louis (2008) and Gelman, Carlin, Stern and Rubin (2004). These books differ from the present work in that they begin with the premise that a Bayesian treatment is called for and then provide guidance on how a Bayesian an- ysis should be executed. Similarly, there are many books written from a classical perspective.



The R Book

The R Book Author Michael J. Crawley
ISBN-10 9781118448960
Release 2012-11-07
Pages 1080
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Hugely successful and popular text presenting an extensive and comprehensive guide for all R users The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research. This edition: Features full colour text and extensive graphics throughout. Introduces a clear structure with numbered section headings to help readers locate information more efficiently. Looks at the evolution of R over the past five years. Features a new chapter on Bayesian Analysis and Meta-Analysis. Presents a fully revised and updated bibliography and reference section. Is supported by an accompanying website allowing examples from the text to be run by the user. Praise for the first edition: ‘…if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.’ (The American Statistician, August 2008) ‘The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book…’ (Professional Pensions, July 2007)