Fourthly, we used multiple starting points, which means that the computations started with different initial values.
Since the starting point is dependent on the treatment sequence, all possible treatment sequences are considered and thus multiple starting points are generated.
A trade-off can be reached by using the standard technique with multiple starting points originated from the possible operation sequences.
We also considered that, in timeframes greater than 1 min, there may be multiple starting points from which the training set can begin.
Numerical maximum likelihood estimation was performed using simulated annealing run from multiple starting points to ensure the global optimum was obtained.
As a useful tool we suggest the use of the R rotine multiStart of R package BB (for solving and optimizing large-scale nonlinear systems) for dealing with multiple starting points to obtain multiple solutions and to test sensitivity to starting values.
One possible alternative is to start the search procedure from multiple starting points simultaneously, thus "growing" in parallel several DPs.
In order to check that, optimization was repeated in those problematic cases with multiple starting points.
A stepwise tree searching strategy that combines independent multiple starting points and efficient algorithms designed for escaping local optima was used to insure sufficient sampling of tree space.
An intensive search of parameter space was performed, fitting from multiple starting points and restricting parameter bounds, to ensure that the best-fitting models were obtained.
Participants were recruited in a regional centre in Western Australia using opportunistic and snowball sampling, with efforts made to recruit women across a range of ages using with multiple starting points to limit bias.
