Download e-book for iPad: Bayesian Forecasting and Dynamic Models (2nd Edition) by Mike West, Jeff Harrison

By Mike West, Jeff Harrison

ISBN-10: 0387947256

ISBN-13: 9780387947259

The second one variation of this ebook contains revised, up to date, and extra fabric at the constitution, thought, and alertness of sessions of dynamic types in Bayesian time sequence research and forecasting. as well as large ranging updates to principal fabric, the second one version comprises many extra routines and covers new themes on the examine and alertness frontiers of Bayesian forecastings.

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Extra resources for Bayesian Forecasting and Dynamic Models (2nd Edition) (Springer Series in Statistics)

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Computing developments have led to wider usage, easing communication with less technically orientated practitioners. We do hold the view that modelling is an art, and particularly so is Bayesian forecasting. 4 Historical Perspective and Bibliographic Comments 31 software package is just that: a package of specific, selected facilities, and the limitations of such software are too easily seen by some as the limitations of the whole approach. Indeed, the early Bayesian forecasting package SHAFT, produced in the 1970s by Colin Stevens, led to a widespread view that Bayesian forecasting was the single model discussed in Harrison and Stevens (1971).

In addition to processing the information deriving from observations and feeding it forward to forecast future development, this probabilistic encoding allows new information from external sources to be formally incorporated in the system. It also extends naturally to allow for expansion or contraction of the parameter vector in open systems, with varying degrees of uncertainty associated with the effects of such external interventions and changes. Further, inferences about system development and change are directly drawn from components of these distributions in a standard statistical manner.

The associated large variance reflects the true increase in uncertainty, leads to less weight being associated with data prior to the change, adapts more quickly to the immediately forthcoming forecast errors, and results in more reliable forecasts. 3 Limiting behaviour and convergence In the closed model, the rate of adaptation to new data, as measured by the adaptive coefficient At , rapidly converges to a constant value as follows. 3. Define r = W/V . As t → ∞, At → A and Ct → C = AV , where lim At = A = t→∞ r 2 1+ 4 −1 .

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Bayesian Forecasting and Dynamic Models (2nd Edition) (Springer Series in Statistics) by Mike West, Jeff Harrison


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