Nonlinear models applied to seed germination of Rhipsalis cereuscula Haw (Cactaceae)

Abstract

The objective of this analysis was to fit germination data of Rhipsalis cereuscula Haw seeds to the Weibull model with three parameters using Frequentist and Bayesian methods. Five parameterizations were compared using the Bayesian analysis to fit a prior distribution. The parameter estimates from the Frequentist method were similar to the Bayesian responses considering the following non-informative a priori distribution for the parameter vectors: gamma (10³, 10³) in the model M₁, normal (0, 10⁶) in the model M₂, uniform (0, L_sup) in the model M₃, exp (μ) in the model M₄ and Lnormal (μ, 10⁶) in the model M₅. However, to achieve the convergence in the models M₄ and M₅, we applied the μ from the estimates of the Frequentist approach. The best models fitted by the Bayesian method were the M₁ and M₃. The adequacy of these models was based on the advantages over the Frequentist method such as the reduced computational efforts and the possibility of comparison.