Bayesian Approach to Neuro-Rough Models
  This paper proposes a neuro-rough model based on multi-layered perceptron and
rough set. The neuro-rough model is then tested on modelling the risk of HIV
from demographic data. The model is formulated using Bayesian framework and
trained using Monte Carlo method and Metropolis criterion. When the model was
tested to estimate the risk of HIV infection given the demographic data it was
found to give the accuracy of 62%. The proposed model is able to combine the
accuracy of the Bayesian MLP model and the transparency of Bayesian rough set
model.
