Algorithms for Maximum Entropy Parameter Estimation

Nidelea Marinela ()
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Nidelea Marinela: „Titu Maiorescu” University of Bucharest

Ovidius University Annals, Economic Sciences Series, 2011, vol. XI, issue 2, 934-937

Abstract: In this paper, we consider a number of algorithms for estimating the parameters of ME models, including iterative scaling, gradient ascent, conjugate gradient, and variable metric methods. Surprisingly, the standardly used iterative scaling algorithms perform quite poorly in comparison to the others, and for all of the test problems, a limitedmemory variable metric algorithm outperformed the other choices. Maximum entropy (ME) models, variously known as log-linear, Gibbs, exponential, and multinomial logit models, provide a general purpose machine learning technique for classification and prediction which has been successfully applied to fields as diverse as computer vision and econometrics.

Keywords: GIS; entropy; ME models; probability; heuristic (search for similar items in EconPapers)
JEL-codes: L63 (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:ovi:oviste:v:xi:y:2011:i:9:p:934-937

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