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A Comparison of Hierarchical Bayes and Empirical Bayes Methods with a Forestry Application

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Gibbs sampling for generating marginal posterior distributions in Bayesian analysis is introduced to the forestry literature. Hierarchical Bayes and (parametric) empirical Bayes methods are compared theoretically and with a practical example. It is shown that in the latter procedures, the error due to estimating the hyperparameters from the marginal distribution of the data is ignored. This results in underestimating the scale of the marginal posterior distributions. For. Sci. 38(2):350-366.
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Keywords: Gibbs sampling; statistical inference

Document Type: Journal Article

Affiliations: Professor and Chairman, Department of Statistics, Rutgers University, New Brunswick, NJ 08903

Publication date: 1992-04-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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