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Predictive Posterior Distributions from a Bayesian Version of a Slash Pine Yield Model

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We formulate a traditional slash pine diameter distribution yield model in a Bayesian framework. We attempt to introduce as few new assumptions as possible. We generate predictive posterior samples for a number of stand variables using the Gibbs sampler. The means of the samples agree well with the predictions from the published model. In addition, our model delivers distributions of outcomes, from which it is easy to establish measures of uncertainty, e.g., Bayesian credible regions. For. Sci. 42(4):456-464.

Keywords: Gibbs sampler; Weibull distribution

Document Type: Journal Article

Affiliations: Department of Statistics, Rutgers University, New Brunswick, NJ 08903-0231

Publication date: 1996-11-01

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  • 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.

    2015 Impact Factor: 1.702
    Ranking: 16 of 66 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
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