Basically the steps described above may be summarised as follows: 
Step 1: Randomly generate the granule weight vector *{gw}*n Step 2: Calculate the posterior probability pn using equation 18 and vector *{gw}*n Step 3: Introduce random changes to vector *{gw}*n to form vector {gw}*n+1* Step 4: Calculate the posterior probability pn+1 using equation 18 and vector {gw}*n+1* Step 5: Accept or reject vector {gw}*n+1* **using equations 21 and 22** 
Step 6**: Go to step 3 and repeat the process until enough samples of distribution in** 
equation 18 have been achieved 

## Experimental Investigation: Modelling Of Hiv

The proposed method is applied to create a model that uses demographic characteristics to estimate the risk of HIV. In the last 20 years, over 60 million people have been infected with HIV, and of those cases, 95% are in developing countries (Lasry et al, 2007). HIV has been identified as the cause of AIDS. Early studies on HIV/AIDS 
focused on the individual characteristics and behaviors in determining HIV risk and Fee and Krieger (1993) refer to this as biomedical individualism. But it has been determined that the study of the distribution of health outcomes and their social determinants is of more importance and this is referred to as social epidemiology (Poundstone et. al., 2004). 

This study uses individual characteristics as well as social and demographic factors in determining the risk of HIV using neuro-rough models formulated using Bayesian approach and trained using Markov Chain Monte Carlo method. Previously, computational intelligence techniques have been used extensively to analyse HIV and Leke et al (2006, 2007) used autoencoder network classifiers, inverse neural networks, as 