The Subjectivity of Scientists and the Bayesian Approach

The Subjectivity of Scientists and the Bayesian Approach
Author :
Publisher : Courier Dover Publications
Total Pages : 292
Release :
ISBN-10 : 9780486810454
ISBN-13 : 0486810453
Rating : 4/5 (54 Downloads)

Synopsis The Subjectivity of Scientists and the Bayesian Approach by : S. James Press

Intriguing examination of works by Aristotle, Galileo, Newton, Pasteur, Einstein, Margaret Mead, and other scientists in terms of subjectivity and the Bayesian approach to statistical analysis. "An insightful work." — Choice. 2001 edition.

The Subjectivity of Scientists and the Bayesian Approach

The Subjectivity of Scientists and the Bayesian Approach
Author :
Publisher : Courier Dover Publications
Total Pages : 292
Release :
ISBN-10 : 9780486802848
ISBN-13 : 0486802841
Rating : 4/5 (48 Downloads)

Synopsis The Subjectivity of Scientists and the Bayesian Approach by : S. James Press

Originally published: New York: John Wiley & Sons, Inc., 2001.

Subjective and Objective Bayesian Statistics

Subjective and Objective Bayesian Statistics
Author :
Publisher : John Wiley & Sons
Total Pages : 591
Release :
ISBN-10 : 9780470317945
ISBN-13 : 0470317949
Rating : 4/5 (45 Downloads)

Synopsis Subjective and Objective Bayesian Statistics by : S. James Press

Ein Wiley-Klassiker über Bayes-Statistik, jetzt in durchgesehener und erweiterter Neuauflage! - Werk spiegelt die stürmische Entwicklung dieses Gebietes innerhalb der letzten Jahre wider - vollständige Darstellung der theoretischen Grundlagen - jetzt ergänzt durch unzählige Anwendungsbeispiele - die wichtigsten modernen Methoden (u. a. hierarchische Modellierung, linear-dynamische Modellierung, Metaanalyse, MCMC-Simulationen) - einzigartige Diskussion der Finetti-Transformierten und anderer Themen, über die man ansonsten nur spärliche Informationen findet - Lösungen zu den Übungsaufgaben sind enthalten

Methods of Multivariate Analysis

Methods of Multivariate Analysis
Author :
Publisher : John Wiley & Sons
Total Pages : 739
Release :
ISBN-10 : 9780471461722
ISBN-13 : 0471461725
Rating : 4/5 (22 Downloads)

Synopsis Methods of Multivariate Analysis by : Alvin C. Rencher

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Methods of Multivariate Analysis was among those chosen. When measuring several variables on a complex experimental unit, it is often necessary to analyze the variables simultaneously, rather than isolate them and consider them individually. Multivariate analysis enables researchers to explore the joint performance of such variables and to determine the effect of each variable in the presence of the others. The Second Edition of Alvin Rencher's Methods of Multivariate Analysis provides students of all statistical backgrounds with both the fundamental and more sophisticated skills necessary to master the discipline. To illustrate multivariate applications, the author provides examples and exercises based on fifty-nine real data sets from a wide variety of scientific fields. Rencher takes a "methods" approach to his subject, with an emphasis on how students and practitioners can employ multivariate analysis in real-life situations. The Second Edition contains revised and updated chapters from the critically acclaimed First Edition as well as brand-new chapters on: Cluster analysis Multidimensional scaling Correspondence analysis Biplots Each chapter contains exercises, with corresponding answers and hints in the appendix, providing students the opportunity to test and extend their understanding of the subject. Methods of Multivariate Analysis provides an authoritative reference for statistics students as well as for practicing scientists and clinicians.

Bayesian Philosophy of Science

Bayesian Philosophy of Science
Author :
Publisher : Oxford University Press
Total Pages : 384
Release :
ISBN-10 : 9780191652226
ISBN-13 : 0191652229
Rating : 4/5 (26 Downloads)

Synopsis Bayesian Philosophy of Science by : Jan Sprenger

How should we reason in science? Jan Sprenger and Stephan Hartmann offer a refreshing take on classical topics in philosophy of science, using a single key concept to explain and to elucidate manifold aspects of scientific reasoning. They present good arguments and good inferences as being characterized by their effect on our rational degrees of belief. Refuting the view that there is no place for subjective attitudes in 'objective science', Sprenger and Hartmann explain the value of convincing evidence in terms of a cycle of variations on the theme of representing rational degrees of belief by means of subjective probabilities (and changing them by Bayesian conditionalization). In doing so, they integrate Bayesian inference—the leading theory of rationality in social science—with the practice of 21st century science. Bayesian Philosophy of Science thereby shows how modeling such attitudes improves our understanding of causes, explanations, confirming evidence, and scientific models in general. It combines a scientifically minded and mathematically sophisticated approach with conceptual analysis and attention to methodological problems of modern science, especially in statistical inference, and is therefore a valuable resource for philosophers and scientific practitioners.

Bayes Rules!

Bayes Rules!
Author :
Publisher : CRC Press
Total Pages : 606
Release :
ISBN-10 : 9781000529562
ISBN-13 : 1000529568
Rating : 4/5 (62 Downloads)

Synopsis Bayes Rules! by : Alicia A. Johnson

Praise for Bayes Rules!: An Introduction to Applied Bayesian Modeling “A thoughtful and entertaining book, and a great way to get started with Bayesian analysis.” Andrew Gelman, Columbia University “The examples are modern, and even many frequentist intro books ignore important topics (like the great p-value debate) that the authors address. The focus on simulation for understanding is excellent.” Amy Herring, Duke University “I sincerely believe that a generation of students will cite this book as inspiration for their use of – and love for – Bayesian statistics. The narrative holds the reader’s attention and flows naturally – almost conversationally. Put simply, this is perhaps the most engaging introductory statistics textbook I have ever read. [It] is a natural choice for an introductory undergraduate course in applied Bayesian statistics." Yue Jiang, Duke University “This is by far the best book I’ve seen on how to (and how to teach students to) do Bayesian modeling and understand the underlying mathematics and computation. The authors build intuition and scaffold ideas expertly, using interesting real case studies, insightful graphics, and clear explanations. The scope of this book is vast – from basic building blocks to hierarchical modeling, but the authors’ thoughtful organization allows the reader to navigate this journey smoothly. And impressively, by the end of the book, one can run sophisticated Bayesian models and actually understand the whys, whats, and hows.” Paul Roback, St. Olaf College “The authors provide a compelling, integrated, accessible, and non-religious introduction to statistical modeling using a Bayesian approach. They outline a principled approach that features computational implementations and model assessment with ethical implications interwoven throughout. Students and instructors will find the conceptual and computational exercises to be fresh and engaging.” Nicholas Horton, Amherst College An engaging, sophisticated, and fun introduction to the field of Bayesian statistics, Bayes Rules!: An Introduction to Applied Bayesian Modeling brings the power of modern Bayesian thinking, modeling, and computing to a broad audience. In particular, the book is an ideal resource for advanced undergraduate statistics students and practitioners with comparable experience. Bayes Rules! empowers readers to weave Bayesian approaches into their everyday practice. Discussions and applications are data driven. A natural progression from fundamental to multivariable, hierarchical models emphasizes a practical and generalizable model building process. The evaluation of these Bayesian models reflects the fact that a data analysis does not exist in a vacuum. Features • Utilizes data-driven examples and exercises. • Emphasizes the iterative model building and evaluation process. • Surveys an interconnected range of multivariable regression and classification models. • Presents fundamental Markov chain Monte Carlo simulation. • Integrates R code, including RStan modeling tools and the bayesrules package. • Encourages readers to tap into their intuition and learn by doing. • Provides a friendly and inclusive introduction to technical Bayesian concepts. • Supports Bayesian applications with foundational Bayesian theory.

Large-Scale Inverse Problems and Quantification of Uncertainty

Large-Scale Inverse Problems and Quantification of Uncertainty
Author :
Publisher : John Wiley & Sons
Total Pages : 403
Release :
ISBN-10 : 9781119957584
ISBN-13 : 1119957583
Rating : 4/5 (84 Downloads)

Synopsis Large-Scale Inverse Problems and Quantification of Uncertainty by : Lorenz Biegler

This book focuses on computational methods for large-scale statistical inverse problems and provides an introduction to statistical Bayesian and frequentist methodologies. Recent research advances for approximation methods are discussed, along with Kalman filtering methods and optimization-based approaches to solving inverse problems. The aim is to cross-fertilize the perspectives of researchers in the areas of data assimilation, statistics, large-scale optimization, applied and computational mathematics, high performance computing, and cutting-edge applications. The solution to large-scale inverse problems critically depends on methods to reduce computational cost. Recent research approaches tackle this challenge in a variety of different ways. Many of the computational frameworks highlighted in this book build upon state-of-the-art methods for simulation of the forward problem, such as, fast Partial Differential Equation (PDE) solvers, reduced-order models and emulators of the forward problem, stochastic spectral approximations, and ensemble-based approximations, as well as exploiting the machinery for large-scale deterministic optimization through adjoint and other sensitivity analysis methods. Key Features: Brings together the perspectives of researchers in areas of inverse problems and data assimilation. Assesses the current state-of-the-art and identify needs and opportunities for future research. Focuses on the computational methods used to analyze and simulate inverse problems. Written by leading experts of inverse problems and uncertainty quantification. Graduate students and researchers working in statistics, mathematics and engineering will benefit from this book.

Bayesian Statistics

Bayesian Statistics
Author :
Publisher :
Total Pages : 264
Release :
ISBN-10 : UOM:39015015723250
ISBN-13 :
Rating : 4/5 (50 Downloads)

Synopsis Bayesian Statistics by : S. James Press

An introduction to Bayesian statistics, with emphasis on interpretation of theory, and application of Bayesian ideas to practical problems. First part covers basic issues and principles, such as subjective probability, Bayesian inference and decision making, the likelihood principle, predictivism, and numerical methods of approximating posterior distributions, and includes a listing of Bayesian computer programs. Second part is devoted to models and applications, including univariate and multivariate regression models, the general linear model, Bayesian classification and discrimination, and a case study of how disputed authorship of some of the Federalist Papers was resolved via Bayesian analysis. Includes biographical material on Thomas Bayes, and a reproduction of Bayes's original essay. Contains exercises.

Bayesian Approach to Intrepreting Archaeological Data

Bayesian Approach to Intrepreting Archaeological Data
Author :
Publisher : Wiley
Total Pages : 402
Release :
ISBN-10 : 0471961973
ISBN-13 : 9780471961970
Rating : 4/5 (73 Downloads)

Synopsis Bayesian Approach to Intrepreting Archaeological Data by : Caitlin E. Buck

Statistics in Practice A new series of practical books outlining the use of statistical techniques in a wide range of application areas: * Human and Biological Sciences * Earth and Environmental Sciences * Industry, Commerce and Finance The authors of this important text explore the processes through which archaeologists analyse their data and how these can be made more rigorous and effective by sound statistical modelling. They assume relatively little previous statistical or mathematical knowledge. Introducing the idea underlying the Bayesian approach to the statistical analysis of data and their subsequent interpretation, the authors demonstrate the major advantage of this approach, i.e. that it allows the incorporation of relevant prior knowledge or beliefs into the analysis. By doing so it provides a logical and coherent way of updating beliefs from those held before observing the data to those held after taking the data into account. To illustrate the power and effectiveness of mathematical and statistical modelling within the Bayesian framework, a variety of real case studies are presented covering areas of common interest to archaeologists. These case studies cover applications in areas such as radiocarbon dating, spatial analysis, provenance studies and other dating methods. Background to these case studies is provided for those readers not so familiar with the subject. Thus, the book provides an examination of the theoretical and practical consequences of Bayesian analysis for examining problems in archaeology. Students of archaeology and related disciplines and professional archaeologists will find the book an informative and practical introduction to the subject.

Fueling Innovation and Discovery

Fueling Innovation and Discovery
Author :
Publisher : National Academies Press
Total Pages : 64
Release :
ISBN-10 : 9780309254731
ISBN-13 : 0309254736
Rating : 4/5 (31 Downloads)

Synopsis Fueling Innovation and Discovery by : National Research Council

The mathematical sciences are part of everyday life. Modern communication, transportation, science, engineering, technology, medicine, manufacturing, security, and finance all depend on the mathematical sciences. Fueling Innovation and Discovery describes recent advances in the mathematical sciences and advances enabled by mathematical sciences research. It is geared toward general readers who would like to know more about ongoing advances in the mathematical sciences and how these advances are changing our understanding of the world, creating new technologies, and transforming industries. Although the mathematical sciences are pervasive, they are often invoked without an explicit awareness of their presence. Prepared as part of the study on the Mathematical Sciences in 2025, a broad assessment of the current state of the mathematical sciences in the United States, Fueling Innovation and Discovery presents mathematical sciences advances in an engaging way. The report describes the contributions that mathematical sciences research has made to advance our understanding of the universe and the human genome. It also explores how the mathematical sciences are contributing to healthcare and national security, and the importance of mathematical knowledge and training to a range of industries, such as information technology and entertainment. Fueling Innovation and Discovery will be of use to policy makers, researchers, business leaders, students, and others interested in learning more about the deep connections between the mathematical sciences and every other aspect of the modern world. To function well in a technologically advanced society, every educated person should be familiar with multiple aspects of the mathematical sciences.