Description

Book Synopsis
This text is concerned with Bayesian learning, inference and forecasting in dynamic environments.

Table of Contents
to the DLM: The First-Order Polynomial Model.- to the DLM: The Dynamic Regression Model.- The Dynamic Linear Model.- Univariate Time Series DLM Theory.- Model Specification and Design.- Polynomial Trend Models.- Seasonal Models.- Regression, Autoregression, and Related Models.- Illustrations and Extensions of Standard DLMs.- Intervention and Monitoring.- Multi-Process Models.- Non-Linear Dynamic Models: Analytic and Numerical Approximations.- Exponential Family Dynamic Models.- Simulation-Based Methods in Dynamic Models.- Multivariate Modelling and Forecasting.- Distribution Theory and Linear Algebra.

Bayesian Forecasting and Dynamic Models Springer Series in Statistics

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    A Paperback by Mike West, Jeff Harrison

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      View other formats and editions of Bayesian Forecasting and Dynamic Models Springer Series in Statistics by Mike West

      Publisher: Springer New York
      Publication Date: 3/8/2013 12:00:00 AM
      ISBN13: 9781475770988, 978-1475770988
      ISBN10: 1475770987
      Also in:
      Economic theory and philosophy Mathematics Stochastics

      Description

      Book Synopsis
      This text is concerned with Bayesian learning, inference and forecasting in dynamic environments.

      Table of Contents
      to the DLM: The First-Order Polynomial Model.- to the DLM: The Dynamic Regression Model.- The Dynamic Linear Model.- Univariate Time Series DLM Theory.- Model Specification and Design.- Polynomial Trend Models.- Seasonal Models.- Regression, Autoregression, and Related Models.- Illustrations and Extensions of Standard DLMs.- Intervention and Monitoring.- Multi-Process Models.- Non-Linear Dynamic Models: Analytic and Numerical Approximations.- Exponential Family Dynamic Models.- Simulation-Based Methods in Dynamic Models.- Multivariate Modelling and Forecasting.- Distribution Theory and Linear Algebra.

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