We also examine the issue of representing the sequence of decision and chance variables, and how it is done in unconstrained influence diagrams, sequential valuation networks, and sequential influence diagrams.
In our model the estimation of the parameters was obtained using the maximum likelihood method (MLE), and assuming that the chance variables are independent and normally distributed.
Crossover probability of (p_{c} = 1) with 50%% chance of variable exchange between offspring, and mutation probability of (p_{m} = 1/5), are used.
In order to increase the chance of variable STRs being predominately represented in STaRRRT, we have restricted the purity to greater than or equal to 90%.
Additionally, it should be noted that backward techniques tend to capitalize on chance, while variables excluded from the final model are no more controlled for their possible confounding effect.
In fact, there is substantial chance that variables will be statistically dependent on batches if a complete randomization is carried out, especially for incomplete and/or unbalanced sample collections.
To avoid a very large number of correlations, which would increase the likelihood of significant correlations by chance, only variables from the open field and histological measures were included in the correlation analysis that showed significant differences or trends in an ANOVA.
Selection probability is the chance that a variable is being selected by applying the lasso shrinkage.
All potential predictors or confounders of interest were entered simultaneously and model building was performed backward using the chance criteria for variable selection.
We then asked whether we observed more nonsynonymous substitutions in epitopes predicted to be targeted than expected by chance (excluding the variable loops of gp120 and the external part of gp41, see Materials and methods).
30 Given the extensive list of patient and surgical variables considered for inclusion in the model, we wanted to ensure that we minimised the possibility of making a type 1 error (rejecting the null hypothesis when it is true)—for example, the chance that a variable identified as being 'significant' in this dataset may not be replicated in other samples of patients.
