  The problem of modeling forest tree growth curves with an artificial neural
network (NN) is examined. The NN parametric form is shown to be a suitable
model if each forest tree plot is assumed to consist of several differently
growing sub-plots. The predictive Bayesian approach is used in estimating the
NN output.
  Data from the correlated curve trend (CCT) experiments are used. The NN
predictions are compared with those of one of the best parametric solutions,
the Schnute model. Analysis of variance (ANOVA) methods are used to evaluate
whether any observed differences are statistically significant. From a
Frequentist perspective the differences between the Schnute and NN approach are
found not to be significant. However, a Bayesian ANOVA indicates that there is
a 93% probability of the NN approach producing better predictions on average.
