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Bayesian Logical Data Analysis for the Physical Sciences

Bayesian Logical Data Analysis for the Physical Sciences Author Phil Gregory
ISBN-10 9781139444286
Release 2005-04-14
Pages
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Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.



Bayesian Logical Data Analysis for the Physical Sciences

Bayesian Logical Data Analysis for the Physical Sciences Author Phil Gregory
ISBN-10 052184150X
Release 2005-04-14
Pages 468
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Increasingly, researchers in many branches of science are coming into contact with Bayesian statistics or Bayesian probability theory. This book provides a clear exposition of the underlying concepts with large numbers of worked examples and problem sets. The book also discusses numerical techniques for implementing the Bayesian calculations, including Markov Chain Monte-Carlo integration and linear and nonlinear least-squares analysis seen from a Bayesian pe rspective. Background material is provided in appendices and supporting Mathematica notebooks are available. Suitable for upper-undergraduates, graduate students, or any serious researcher in physical sciences or engineering.



Data Analysis Techniques for Physical Scientists

Data Analysis Techniques for Physical Scientists Author Claude A. Pruneau
ISBN-10 9781108267885
Release 2017-10-05
Pages
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A comprehensive guide to data analysis techniques for physical scientists, providing a valuable resource for advanced undergraduate and graduate students, as well as seasoned researchers. The book begins with an extensive discussion of the foundational concepts and methods of probability and statistics under both the frequentist and Bayesian interpretations of probability. It next presents basic concepts and techniques used for measurements of particle production cross-sections, correlation functions, and particle identification. Much attention is devoted to notions of statistical and systematic errors, beginning with intuitive discussions and progressively introducing the more formal concepts of confidence intervals, credible range, and hypothesis testing. The book also includes an in-depth discussion of the methods used to unfold or correct data for instrumental effects associated with measurement and process noise as well as particle and event losses, before ending with a presentation of elementary Monte Carlo techniques.



Bayesian Probability Theory

Bayesian Probability Theory Author Wolfgang von der Linden
ISBN-10 9781107035904
Release 2014-06-12
Pages 649
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Covering all aspects of probability theory, statistics and data analysis from a Bayesian perspective for graduate students and researchers.



Bayesian Statistics

Bayesian Statistics Author Peter M. Lee
ISBN-10 9781118359778
Release 2012-06-25
Pages 488
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Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques. This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJMCMC), providing a concise account of the way in which the Bayesian approach to statistics develops as well as how it contrasts with the conventional approach. The theory is built up step by step, and important notions such as sufficiency are brought out of a discussion of the salient features of specific examples. This edition: Includes expanded coverage of Gibbs sampling, including more numerical examples and treatments of OpenBUGS, R2WinBUGS and R2OpenBUGS. Presents significant new material on recent techniques such as Bayesian importance sampling, variational Bayes, Approximate Bayesian Computation (ABC) and Reversible Jump Markov Chain Monte Carlo (RJMCMC). Provides extensive examples throughout the book to complement the theory presented. Accompanied by a supporting website featuring new material and solutions. More and more students are realizing that they need to learn Bayesian statistics to meet their academic and professional goals. This book is best suited for use as a main text in courses on Bayesian statistics for third and fourth year undergraduates and postgraduate students.



Modern Statistical Methods for Astronomy

Modern Statistical Methods for Astronomy Author Eric D. Feigelson
ISBN-10 9780521767279
Release 2012-07-12
Pages 476
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"Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Yet most astronomers still use a narrow suite of traditional statistical methods. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to advanced statistics through ready-to-use code in the public-domain R statistical software environment"--



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.



Data Analysis

Data Analysis Author Devinderjit Sivia
ISBN-10 9780191546709
Release 2006-06-01
Pages 264
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Data Analysis has been writing in one form or another for most of life. You can find so many inspiration from Data Analysis also informative, and entertaining. Click DOWNLOAD or Read Online button to get full Data Analysis book for free.



Nonlinear and Nonstationary Signal Processing

Nonlinear and Nonstationary Signal Processing Author W. J. Fitzgerald
ISBN-10 0521800447
Release 2000
Pages 471
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Signal processing, nonlinear data analysis, nonlinear time series, nonstationary processes.



Applied Predictive Modeling

Applied Predictive Modeling Author Max Kuhn
ISBN-10 9781461468493
Release 2013-05-17
Pages 600
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Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.



Innovations in Federal Statistics

Innovations in Federal Statistics Author National Academies of Sciences, Engineering, and Medicine
ISBN-10 9780309454285
Release 2017-04-21
Pages 150
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Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels. Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm.



Unit Operations

Unit Operations Author Ian Bogost
ISBN-10 9780262261890
Release 2008-01-25
Pages 264
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In Unit Operations, Ian Bogost argues that similar principles underlie both literary theory and computation, proposing a literary-technical theory that can be used to analyze particular videogames. Moreover, this approach can be applied beyond videogames: Bogost suggests that any medium -- from videogames to poetry, literature, cinema, or art -- can be read as a configurative system of discrete, interlocking units of meaning, and he illustrates this method of analysis with examples from all these fields. The marriage of literary theory and information technology, he argues, will help humanists take technology more seriously and hep technologists better understand software and videogames as cultural artifacts. This approach is especially useful for the comparative analysis of digital and nondigital artifacts and allows scholars from other fields who are interested in studying videogames to avoid the esoteric isolation of "game studies."The richness of Bogost's comparative approach can be seen in his discussions of works by such philosophers and theorists as Plato, Badiou, Zizek, and McLuhan, and in his analysis of numerous videogames including Pong, Half-Life, and Star Wars Galaxies. Bogost draws on object technology and complex adaptive systems theory for his method of unit analysis, underscoring the configurative aspects of a wide variety of human processes. His extended analysis of freedom in large virtual spaces examines Grand Theft Auto 3, The Legend of Zelda, Flaubert's Madame Bovary, and Joyce's Ulysses. In Unit Operations, Bogost not only offers a new methodology for videogame criticism but argues for the possibility of real collaboration between the humanities and information technology.



Practical Statistics for Astronomers

Practical Statistics for Astronomers Author J. V. Wall
ISBN-10 0521456169
Release 2003-11-13
Pages 277
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Presents the most relevant statistical and probabilistic machinery for use in observational astronomy.



Bayes Or Bust

Bayes Or Bust Author John Earman
ISBN-10 0262050463
Release 1992
Pages 272
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There is currently no viable alternative to the Bayesian analysis of scientific inference, yet the available versions of Bayesianism fail to do justice to several aspects of the testing and confirmation of scientific hypotheses. Bayes or Bust? provides the first balanced treatment of the complex set of issues involved in this nagging conundrum in the philosophy of science. Both Bayesians and anti-Bayesians will find a wealth of new insights on topics ranging from Bayes's original paper to contemporary formal learning theory. In a paper published posthumously in 1763, the Reverend Thomas Bayes made a seminal contribution to the understanding of "analogical or inductive reasoning." Building on his insights, modem Bayesians have developed an account of scientific inference that has attracted numerous champions as well as numerous detractors. Earman argues that Bayesianism provides the best hope for a comprehensive and unified account of scientific inference, yet the presently available versions of Bayesianisin fail to do justice to several aspects of the testing and confirming of scientific theories and hypotheses. By focusing on the need for a resolution to this impasse, Earman sharpens the issues on which a resolution turns. John Earman is Professor of History and Philosophy of Science at the University of Pittsburgh.



Using Science as Evidence in Public Policy

Using Science as Evidence in Public Policy Author Center for Education
ISBN-10 9780309261616
Release 2012-10-31
Pages 122
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Using Science as Evidence in Public Policy encourages scientists to think differently about the use of scientific evidence in policy making. This report investigates why scientific evidence is important to policy making and argues that an extensive body of research on knowledge utilization has not led to any widely accepted explanation of what it means to use science in public policy. Using Science as Evidence in Public Policy identifies the gaps in our understanding and develops a framework for a new field of research to fill those gaps. For social scientists in a number of specialized fields, whether established scholars or Ph.D. students, Using Science as Evidence in Public Policy shows how to bring their expertise to bear on the study of using science to inform public policy. More generally, this report will be of special interest to scientists who want to see their research used in policy making, offering guidance on what is required beyond producing quality research, beyond translating results into more understandable terms, and beyond brokering the results through intermediaries, such as think tanks, lobbyists, and advocacy groups. For administrators and faculty in public policy programs and schools, Using Science as Evidence in Public Policy identifies critical elements of instruction that will better equip graduates to promote the use of science in policy making.



Studies in the Methodology and Foundations of Science

Studies in the Methodology and Foundations of Science Author Patrick Suppes
ISBN-10 9789401731737
Release 2013-03-09
Pages 475
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The twenty-three papers collected in tbis volume represent an important part of my published work up to the date of this volume. I have not arranged the paper chronologically, but under four main headings. Part I contains five papers on methodology concerned with models and measurement in the sciences. This part also contains the first paper I published, 'A Set of Independent Axioms for Extensive Quantities', in Portugaliae Mathematica in 1951. Part 11 also is concerned with methodology and ineludes six papers on probability and utility. It is not always easy to separate papers on probability and utility from papers on measurement, because of the elose connection between the two subjects, but Artieles 6 and 8, even though they have elose relations to measurement, seem more properly to belong in Part 11, because they are concerned with substantive questions about probability and utility. The last two parts are concerned with the foundations of physics and the foundations of psychology. I have used the term foundations rather than philosophy, because the papers are mainly concerned with specific axiomatic formulations for particular parts of physics or of psychology, and it seems to me that the termfoundations more appropriately describes such constructive axiomatic ventures. Part 111 contains four papers on the foundations of physics. The first paper deals with foundations of special relativity and the last three with the role ofprobability in quantum mechanics.



MATLAB for Neuroscientists

MATLAB for Neuroscientists Author Pascal Wallisch
ISBN-10 9780123838377
Release 2014-01-09
Pages 570
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MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. The first complete volume on MATLAB focusing on neuroscience and psychology applications Problem-based approach with many examples from neuroscience and cognitive psychology using real data Illustrated in full color throughout Careful tutorial approach, by authors who are award-winning educators with strong teaching experience