Souza, Reinaldo Castro (1978) A Bayesian entropy approach to forecasting. PhD thesis, University of Warwick.

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Abstract

This thesis describes a new approach to steady-state forecasting models based on Bayesian principles and Information Theory. Shannon's entropy function and Jaynes' principle of maximum entropy are the essen­tial results borrowed from Information Theory and are extensively used in the model formulation. The Bayesian Entropy Forecasting (BEF) models obtained in this way extend beyond the constraints of normality and linearity required in all existing forecasting methods. In this sense, it reduces in the normal case to the well known Harrison and Stevens steady-state model. Examples of such models are presented, including the Poisson-gamma process, the Binomial-Beta process and the Truncated Normal process. For all of these, numerical applications using real and simulated data are shown, including further analyses of epidemic data of Cliff et al, (1975).

Item Type:	Thesis (PhD)
Subjects:	Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Bayesian statistical decision theory, Forecasting -- Mathematical models, Forecasting -- Methodology, Entropy (Information theory), Entropy -- Mathematics
Official Date:	November 1978
Dates:	Date Event November 1978 Submitted
Institution:	University of Warwick
Theses Department:	Department of Statistics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Harrison, P. J.
Sponsors:	CAPES (Organization : Brazil) ; Pontifícia Universidade Católica do Rio de Janeiro
Extent:	230 leaves : illustrations
Language:	eng

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