plausibility that a person is HIV positive and -1 indicating 100% plausibility of HIV 
negative. When training the rough set models using Markov Chain Monte Carlo, 500 samples were accepted and retained meaning that 500 sets of rules where each set contained 50 up to 550 numbers of rules with an average of 222 rules as can be seen in Figure 1. 500 samples were retained because the simulation had converged to a stationary distribution. This figure must be interpreted in the light of the fact on calculating the posterior probability we used the knowledge that fewer rules are more desirable than many. Therefore, the Bayesian rough set framework is able to select the number of rules in addition to the partition sizes. 

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