Bayesian transformed symmetric models to describe the height growth of eucalyptus urophylla.models (Research, Society and Development, 2020)
It is presented in this work the growth model nonlinear Chapman-Richards with distribution of errors following the new class of symmetric models processed and Bayesian inference for the parameters. The objective was to apply this structure, via Metropolis-Hastings algorithm, in order to select the equation that best predicted heights of clones of Eucalyptus urophilla experiment established at the Agronomic Institute of Pernambuco (IPA) in the city of Araripina. The Gypsum Pole of Araripe is an industrial zone, located on the upper interior of Pernambuco, which consumes large amount of wood from native vegetation (caatinga) for calcination of gypsum. In this scenario, there is great need for a solution, economically and environmentally feasible that allows minimizing the pressure on native vegetation. The generus Eucalyptus presents itself as an alternative for rapid development and versatility. The height has proven to be an important factor in prognosis of productivity and selection of clones best adapted. One of the main growth curves, is the Chapman-Richards model with normal distribution for errors. However, some alternatives have been proposed in order to reduce the inﬂuence of atypical observations generated by this model. The data were taken from a plantation, with 72 months. After selecting the best equation, was shown some convergence of graphics and other parameters that show the fit to the data model transformed symmetric Student’s t with 5 degrees of freedom in the parameters using Bayesian inference.