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Author
Description
"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods."
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis,...
Author
Description
This well-known and highly respected introduction to decision theory was developed at Stanford University. It furnishes a simple and clear-cut method of exhibiting the fundamental aspects of statistical problems. Beginners will find this treatment a motivating introduction to important mathematical notions such as set, function, and convexity.
Author
Description
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 principles of Bayesian modeling with detailed guidance on the practical implementation of key principles.
The...
Author
Description
The author has built an innovative system for predicting baseball performance, predicted the 2008 election within a hair's breadth, and has become a national sensation as a blogger. Drawing on his own groundbreaking work, he examines the world of prediction. Human beings have to make plans and strategize for the future. As the pace of our lives becomes faster and faster, we have to do so more often and more quickly. But are our predictions any good?...
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“Implementing Models of Financial Derivatives” is a comprehensive treatment of advanced implementation techniques in VBA for models of financial derivatives. Aimed at readers who are already familiar with the basics of VBA it emphasizes a fully object oriented approach to valuation applications, chiefly in the context of Monte Carlo simulation but also more broadly for lattice and PDE methods. Its unique approach to valuation, emphasizing effective...
Author
Description
This book has two main purposes. On the one hand, it provides a concise and systematic development of the theory of lower previsions, based on the concept of acceptability, in spirit of the work of Williams and Walley. On the other hand, it also extends this theory to deal with unbounded quantities, which abound in practical applications.
Following Williams, we start out with sets of acceptable gambles. From those, we derive rationality criteria-avoiding...
Author
Description
Peter E. Rossi is the James Collins Professor of Marketing, Economics, and Statistics at UCLA's Anderson School of Management. He has published widely in marketing, economics, statistics, and econometrics and is a coauthor of Bayesian Statistics and Marketing.
This book reviews and develops Bayesian non-parametric and semi-parametric methods for applications in microeconometrics and quantitative marketing. Most econometric models used in microeconomics...
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Series
Description
The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances...
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Description
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
Author
Description
This book provides clear instructions to researchers on how to apply Structural Equation Models (SEMs) for analyzing the inter relationships between observed and latent variables.
“Basic and Advanced Bayesian Structural Equation Modeling” introduces basic and advanced SEMs for analyzing various kinds of complex data, such as ordered and unordered categorical data, multilevel data, mixture data, longitudinal data, highly non-normal data, as well...
Author
Description
A balanced treatment of the theories, methodologies, and design issues involved in clinical trials using statistical methods
There has been enormous interest and development in Bayesian adaptive designs, especially for early phases of clinical trials. However, for phase III trials, frequentist methods still play a dominant role through controlling type I and type II errors in the hypothesis testing framework. From practical perspectives, Clinical...
Author
Publication Date
2019.
Physical Desc
x, 306 pages : illustrations, charts ; 25 cm.
Description
Thomas Bayes devised a theorem that allowed him to assign probabilities to events that had never happened before, and it is being used in contemporary thought to answer questions about human existence.
Author
Series
Studies in mathematical geology volume 6
Description
This scholarly introductory treatment explores the fundamentals of modern geostatistics, viewing them as the product of the advancement of the epistemic status of stochastic data analysis. The book's main focus is the Bayesian maximum entropy approach for studying spatiotemporal distributions of natural variables, an approach that offers readers a deeper understanding of the role of geostatistics in improved mathematical models of scientific mapping....
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Description
Spatial and Spatio-Temporal Bayesian Models with R-INLA provides a much needed, practically oriented & innovative presentation of the combination of Bayesian methodology and spatial statistics. The authors combine an introduction to Bayesian theory and methodology with a focus on the spatial and spatio-temporal models used within the Bayesian framework and a series of practical examples which allow the reader to link the statistical theory presented...
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Series
Mathematics in science and engineering volume 64
Description
This unified treatment of linear and nonlinear filtering theory presents material previously available only in journals, and in terms accessible to engineering students. Its sole prerequisites are advanced calculus, the theory of ordinary differential equations, and matrix analysis. Although theory is emphasized, the text discusses numerous practical applications as well. Taking the state-space approach to filtering, this text models dynamical systems...
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Description
Fine-tune your marketing research with this cutting-edge statistical toolkit
Bayesian Statistics and Marketing illustrates the potential for applying a Bayesian approach to some of the most challenging and important problems in marketing. Analyzing household and consumer data, predicting product performance, and custom-targeting campaigns are only a few of the areas in which Bayesian approaches promise revolutionary results. This book provides a...
Author
Description
Presents an introduction to Bayesian statistics, presents an emphasis on Bayesian methods (prior and posterior), Bayes estimation, prediction, MCMC,Bayesian regression, and Bayesian analysis of statistical modelsof dependence, and features a focus on copulas for risk management
Introduction to Bayesian Estimation and Copula Models of Dependence emphasizes the applications of Bayesian analysis to copula modeling and equips readers with the tools...
Author
Description
Forty years ago, Israeli psychologists Daniel Kahneman and Amos Tversky wrote a series of studies undoing our assumptions about the decision-making process. Their papers showed the ways in which the human mind erred, systematically, when forced to make judgments in uncertain situations. Their work created the field of behavioral economics, revolutionized Big Data studies, advanced evidence-based medicine, led to a new approach to government regulation,...
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Description
Demonstrates how to solve reliability problems using practical applications of Bayesian models
This self-contained reference provides fundamental knowledge of Bayesian reliability and utilizes numerous examples to show how Bayesian models can solve real life reliability problems. It teaches engineers and scientists exactly what Bayesian analysis is, what its benefits are, and how they can apply the methods to solve their own problems. To help readers...
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Series
Description
Presents the Bayesian approach to statistical signal processing for a variety of useful model sets.
This book aims to give readers a unified Bayesian treatment starting from the basics (Baye's rule) to the more advanced (Monte Carlo sampling), evolving to the next-generation model-based techniques (sequential Monte Carlo sampling). This next edition incorporates a new chapter on "Sequential Bayesian Detection," a new section on "Ensemble Kalman Filters"...




