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2005-10-17Buch DOI: 10.18452/3609
Modeling the Learning from Repeated Samples
dc.contributor.authorPapalia, Rosa Bernardini
dc.date.accessioned2017-06-15T21:32:57Z
dc.date.available2017-06-15T21:32:57Z
dc.date.created2005-10-17
dc.date.issued2005-10-17
dc.identifier.issn1436-1086
dc.identifier.urihttp://edoc.hu-berlin.de/18452/4261
dc.description.abstractIn this study we illustrate a Maximum Entropy (ME) methodology for modeling incomplete information and learning from repeated samples. The basis for this method has its roots in information theory and builds on the classical maximum entropy work of Janes (1957). We illustrate the use of this approach, describe how to impose restrictions on the estimator, and how to examine the sensitivity of ME estimates to the parameter and error bounds. Our objective is to show how empirical measures of the value of information for microeconomic models can be estimated in the maximum entropy view.eng
dc.language.isoeng
dc.publisherHumboldt-Universität zu Berlin, Wirtschaftswissenschaftliche Fakultät
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subjectGeneralized Maximum Entropyeng
dc.subjectGeneralized Cross Entropyeng
dc.subjectRepeated Sampleseng
dc.subjectMicroeconometric modelseng
dc.subject.ddc330 Wirtschaft
dc.titleModeling the Learning from Repeated Samples
dc.typebook
dc.subtitlea Generalized Cross Entropy Approach
dc.identifier.urnurn:nbn:de:kobv:11-10050297
dc.identifier.doihttp://dx.doi.org/10.18452/3609
local.edoc.container-titleSonderforschungsbereich 373: Quantification and Simulation of Economic Processes
local.edoc.pages10
local.edoc.type-nameBuch
local.edoc.container-typeseries
local.edoc.container-type-nameSchriftenreihe
local.edoc.container-volume2003
local.edoc.container-issue29
local.edoc.container-year2003
local.edoc.container-erstkatid2135319-0

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