A Bayesian methodology has been previously applied to MRF reconstructions to analyze subvoxel T1 and T2 distributions. The multidimensional results from this algorithm are difficult to visualize. We propose to apply this Bayesian approach in the brain to create T1 and T2 Gaussian distributions to represent various tissue types. Using these distributions as prior information, the Bayesian methodology is applied over the brain with a smaller dictionary. Results from this Bayesian approach with a smaller dictionary are weighted by the Gaussian probabilities and summed to create tissue probability maps in normal volunteers and a brain tumor patient.