Bayesian inference for Johnson's SB and Weibull distributions

The four-parameter Johnson's SB (JSB) and three-parameter Weibull distributions have received significant attention in the field of forestry for characterising diameters at breast height (DBH). This study suggests Bayesian method estimating parameters JSB distribution. maximum likelihood approach uses iterative methods such as a Newton–Raphson (NR) algorithm maximising logarithm function. However, there is no guarantee that NR converges. Through simulation, this verified distribution sometimes fails to converge. Further, estimators presented herein were shown be robust with respect initial values estimate efficiently. performance was compared paradigm when these models fitted DBH data three plots randomly selected from established 107 mixed-age ponderosa pine (Pinus Dougl. ex Laws.) scattered western juniper (Juniperus occidentalis Hook.) Malheur National Forest on south end Blue Mountains near Burns, Oregon, USA. demonstrated superior plots. Moreover, outperformed moment, conditional likelihood, two-percentile all simultaneously.