Abstract
Abstract 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
