We could have chosen a cutoff on these probabilities to define change points, but it would have necessitated the selection of an arbitrary threshold value, while using the regresion model with the highest posterior probability to determine change points can circumvent the problem.
Using Monte Carlo techniques, SaTScan assigned relative risk probabilities to defined block groups to detect both the location of clusters and evaluate their statistical significance.
For example, McCarthy and Possingham 20 used beta probability distributions to define estimates of the probability of revegetation success for two different management actions.
(They appeal to a more complicated probability function to define a notion of "subjective rightness" for decisionmakers who are ignorant of the objective chances).
In the examples given in Section 1, in particular [13 17], the problems could have been formulated similarly using a Bayesian approach because the probabilities used to define the hypotheses were well-defined.
They show that every preference relation obeying certain axioms can be represented by the probabilities used to define the lotteries, together with a utility function which is unique up to positive linear transformation.
Offset terms were incorporated into models using the SAS procedure, PROC GENMOD, to account for the sampling probabilities used to define eligible cases and controls [ 15].
We then applied the risk equations in the study in two ways; as variable estimates for the decision analytical model (in determining transition probabilities), and to define the prioritisation strategies (that includes biomarker information) used to rank patients.
Key function of such a probability model is to define the probability that a gene is regulated by a given transcription factor.
Additionally, a threshold value for classification probability was optimized to define an "unclassified" class that allows using this algorithm even when the training sample does not represent an exhaustive sample of local richness.
