5. The method according to claim 1 , wherein: the posterior Pr({C (j) }|F (j) } j=1 m ,{S (j) } j=1 m ,{R (ij) } i,j=1 m ,{tilde over (Ω)}) is computed using the Gibbs sampler.
6. The method according to claim 5 , wherein the likelihood function is maximized by iteratively updating {tilde over (Ω)}=Ω over the range of i and j, until convergence is achieved, with update rules: Λ hp ( 1 ) = Pr ⁡ ( C hp ( 1 ) = 1 | F , S , R , Ω ~ ) , ⁢ Θ · g = ∑ p = 1 n 1 ⁢ F · p ⁢ Pr ⁡ ( C gp ( 1 ) = 1 | F , S , R , Ω ~ ) ∑ p = 1 n 1 ⁢ Pr ⁡ ( C gp ( 1 ) = 1 | F , S , R , Ω ~ ) Γ gh = ∑ p , q = 1 n 1 ⁢ S pq ⁢ Pr ⁡ ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 | F , S , R , Ω ~ ) ∑ p , q = 1 n 1 ⁢ Pr ⁡ ( C gp ( 1 ) = 1 , C hq ( 1 ) = 1 | F , S , R , Ω ~ ) , to estimate unknown parameters of a joint probability distribution equation over the observations and latent indicators.