Comparison of Normal, Logistic, Laplace, and Student's t distributions for experimental error in the Bayesian description of dry matter accumulation in Allium sativum

This study assessed distributions associated with Bayesian nonlinear modeling error in the description of total plant dry matter accumulation (TDMA) of Allium sativum as a function of days after planting (DAP). According to the DIC criterion, Logistic and Gompertz models that use student's t distribution error exhibited the highest DIC with logistic error distribution. In general, the difference of DIC in all the scenarios was not more than 5. The Bayes factor (BF) criterion showed no difference in the Logistic and Gompertz model fit when four distributions are used for the errors, where BF values do not exceed 2. Posterior distributions and the usual estimators of Logistic and Gompertz model parameters were similar even for varied error distribution. In summary, there was no difference in the use of 4 distributions associated with the modeling error of garlic plant growth by the Bayes factor, whereby the results showed that alternating between error distributions significantly changes the number of Markov Chain Monte Carlo (MCMC) iterations.