Predictive posterior distributions from a Bayesian version of a slash pine yield model

Abstract

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.