  The four-parameter Johnson's SB (JSB) and three-parameter Weibull
distributions have received much attention in the field of forestry for
characterizing diameters at breast height (DBH). In this work, we suggest the
Bayesian method for estimating parameters of the JBS distribution. The maximum
likelihood approach uses iterative methods such as Newton-Raphson (NR)
algorithm for maximizing the logarithm of the likelihood function. But there is
no guarantee that the NR method converges. This fact that the NR method for
estimating the parameters of the JSB distribution sometimes fails to converge
was verified through simulation in this study. Further, it was shown that the
Bayesian estimators presented in this work were robust with respect to the
initial values and estimate the parameters of the JSB distribution efficiently.
The performance of the JSB and three-parameter Weibull distributions was
compared in a Bayesian paradigm when these models were fitted to DBH data of
three plots that randomly selected from a study established in 107 plots of
mixed-age ponderosa pine (Pinus ponderosa Dougl. ex Laws.) with scattered
western junipers at the Malheur National Forest in south end of the Blue
Mountains near Burns, Oregon, USA. Bayesian paradigm demonstrated that JBS was
superior model than the three-parameter Weibull for characterizing the DBH
distribution when these models were fitted to the DBH data of the three plots.
