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Title: A Bayesian entropy approach to forecasting
Author: Souza, Reinaldo Castro
ISNI:       0000 0001 3473 4027
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 1978
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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).
Supervisor: Not available Sponsor: CAPES (Brazil) ; Pontifícia Universidade Católica do Rio de Janeiro
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QA Mathematics