For discrimination with binary variables, reformulated full and first order Bahadur model with incomplete observations are presented. This allows prior probabilities associated with multiple populations to be estimated for the sample-based classification rule. The EM algorithm is adopted to provide the maximum likelihood estimates of the parameters of interest. Some experiences with the models are evaluated and discussed.