Data/hora: 05/06/2024 13:23:13

Provedor de dados: | 31
País: | United States
Título: | INFERRING THE NUTRIENT CONTENT OF FOOD WITH PRIOR INFORMATION
Autores: | LaFrance,   Jeffrey T.
Data: | 1998-12-16
Ano: | 1998
Palavras-chave: | Agricultural Farm and Food Policy; Bayesian Method of  Moments; Generalized Maximum Entropy; Kullback-Leibler Cross Entropy; Nutrient Content; Food Consumption/Nutrition/Food Safety.
Resumo: | Given measurements on the nutrient content of the U.S. food supply and a coherent reduced form empirical model of the demand for foods, we can analyze the effect of agricultural farm and food policy on nutrition. Using unpublished documents from the HNIS, estimates of the percentages of seventeen nutrients supplied by twenty-one foods were compiled for the period 1952-1983. The Bayesian Method of Moments is applied to this data set to obtain a proper prior for the purpose of drawing year-to-year inferences about the nutrient content of the U.S. food supply for the period 1909-1994. Information theory and the Kullback-Leibler cross entropy criterion are used to formalize the inference problem.
Tipo: | Working or Discussion Paper
Idioma: | Inglês

Identificador:

1063

http://purl.umn.edu/25067

Editor: | AgEcon Search

Relação:

University of California, Berkeley&gt;Department of Agricultural and Resource Economics&gt;CUDARE Working Papers

Working Paper 863

Formato:

20

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