1# Library to Find Best eBook for Read
Author : Claudia Cargnoni
Release : 1995
Genre : Bayesian statistical decision theory
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Bayesian Forecasting of Multinomial Time Series Through Conditionally Gaussian Dynamic Models by : Claudia Cargnoni
Download or read book Bayesian Forecasting of Multinomial Time Series Through Conditionally Gaussian Dynamic Models written by Claudia Cargnoni. This book was released on 1995. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Mike West
Release : 2013-06-29
Genre : Mathematics
Kind : eBook
Book Rating : 650/5 ( reviews)
Book Synopsis Bayesian Forecasting and Dynamic Models by : Mike West
Download or read book Bayesian Forecasting and Dynamic Models written by Mike West. This book was released on 2013-06-29. Available in PDF, EPUB and Kindle. Book excerpt: In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter.
Author : Mike West
Release : 2006-05-02
Genre : Mathematics
Kind : eBook
Book Rating : 776/5 ( reviews)
Book Synopsis Bayesian Forecasting and Dynamic Models by : Mike West
Download or read book Bayesian Forecasting and Dynamic Models written by Mike West. This book was released on 2006-05-02. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Author : Nalini Ravishanker
Release : 2022-08-10
Genre : Mathematics
Kind : eBook
Book Rating : 606/5 ( reviews)
Book Synopsis Dynamic Time Series Models using R-INLA by : Nalini Ravishanker
Download or read book Dynamic Time Series Models using R-INLA written by Nalini Ravishanker. This book was released on 2022-08-10. Available in PDF, EPUB and Kindle. Book excerpt: Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework. The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series. Key Features: Introduction and overview of R-INLA for time series analysis. Gaussian and non-Gaussian state space models for time series. State space models for time series with exogenous predictors. Hierarchical models for a potentially large set of time series. Dynamic modelling of stochastic volatility and spatio-temporal dependence.
Author : Andy Pole
Release : 2018-10-08
Genre : Business & Economics
Kind : eBook
Book Rating : 438/5 ( reviews)
Book Synopsis Applied Bayesian Forecasting and Time Series Analysis by : Andy Pole
Download or read book Applied Bayesian Forecasting and Time Series Analysis written by Andy Pole. This book was released on 2018-10-08. Available in PDF, EPUB and Kindle. Book excerpt: 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.
