Applied Bayesian Forecasting And Time Series Analysis
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Applied Bayesian Forecasting and Time Series Analysis
Author | : Andy Pole,Mike West,Jeff Harrison |
Publsiher | : CRC Press |
Total Pages | : 432 |
Release | : 2018-10-08 |
Genre | : Business & Economics |
ISBN | : 9781482267433 |
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Practical in its approach, Applied Bayesian Forecasting and Time Series Analysis provides the theories, methods, and tools necessary for forecasting and the analysis of time series. The authors unify the concepts, model forms, and modeling requirements within the framework of the dynamic linear mode (DLM). They include a complete theoretical development of the DLM and illustrate each step with analysis of time series data. Using real data sets the authors: Explore diverse aspects of time series, including how to identify, structure, explain observed behavior, model structures and behaviors, and interpret analyses to make informed forecasts Illustrate concepts such as component decomposition, fundamental model forms including trends and cycles, and practical modeling requirements for routine change and unusual events Conduct all analyses in the BATS computer programs, furnishing online that program and the more than 50 data sets used in the text The result is a clear presentation of the Bayesian paradigm: quantified subjective judgements derived from selected models applied to time series observations. Accessible to undergraduates, this unique volume also offers complete guidelines valuable to researchers, practitioners, and advanced students in statistics, operations research, and engineering.
Bayesian Forecasting and Dynamic Models
Author | : Mike West,Jeff Harrison |
Publsiher | : Springer Science & Business Media |
Total Pages | : 682 |
Release | : 2006-05-02 |
Genre | : Mathematics |
ISBN | : 9780387227771 |
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This text is concerned with Bayesian learning, inference and forecasting in dynamic environments. We describe the structure and theory of classes of dynamic models and their uses in forecasting and time series analysis. The principles, models and methods of Bayesian forecasting and time - ries analysis have been developed extensively during the last thirty years. Thisdevelopmenthasinvolvedthoroughinvestigationofmathematicaland statistical aspects of forecasting models and related techniques. With this has come experience with applications in a variety of areas in commercial, industrial, scienti?c, and socio-economic ?elds. Much of the technical - velopment has been driven by the needs of forecasting practitioners and applied researchers. As a result, there now exists a relatively complete statistical and mathematical framework, presented and illustrated here. In writing and revising this book, our primary goals have been to present a reasonably comprehensive view of Bayesian ideas and methods in m- elling and forecasting, particularly to provide a solid reference source for advanced university students and research workers.
Applied Bayesian Forecasting
Author | : Routledge, Chapman & Hall, Incorporated |
Publsiher | : Unknown |
Total Pages | : 135 |
Release | : 2024 |
Genre | : Electronic Book |
ISBN | : 0412040018 |
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Time Series
Author | : Raquel Prado,Mike West |
Publsiher | : CRC Press |
Total Pages | : 375 |
Release | : 2010-05-21 |
Genre | : Mathematics |
ISBN | : 9781439882757 |
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Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian t
Time Series
Author | : Raquel Prado,Marco A. R. Ferreira,Mike West |
Publsiher | : CRC Press |
Total Pages | : 473 |
Release | : 2021-07-27 |
Genre | : Mathematics |
ISBN | : 9781498747042 |
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• Expanded on aspects of core model theory and methodology. • Multiple new examples and exercises. • Detailed development of dynamic factor models. • Updated discussion and connections with recent and current research frontiers.
The Oxford Handbook of Applied Bayesian Analysis
Author | : Anthony O' Hagan,Mike West |
Publsiher | : Oxford University Press |
Total Pages | : 924 |
Release | : 2010-03-18 |
Genre | : Business & Economics |
ISBN | : 9780199548903 |
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Bayesian Statistics is a dynamic and fast-growing area of statistical research with wide-ranging and far-reaching applications across science, technology, commerce, and industry. This Handbook explores contemporary Bayesian analysis across a variety of techniques and application areas.
Bayesian Data Analysis Third Edition
Author | : Andrew Gelman,John B. Carlin,Hal S. Stern,David B. Dunson,Aki Vehtari,Donald B. Rubin |
Publsiher | : CRC Press |
Total Pages | : 677 |
Release | : 2013-11-01 |
Genre | : Mathematics |
ISBN | : 9781439840955 |
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Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Bayesian Time Series Models
Author | : David Barber,A. Taylan Cemgil,Silvia Chiappa |
Publsiher | : Cambridge University Press |
Total Pages | : 432 |
Release | : 2011-08-11 |
Genre | : Computers |
ISBN | : 9780521196765 |
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The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.