Estimation methods by stochastic model in binary and ternary AHP

New stochastic models for binary and ternary AHP are proposed, and further minimax and least square estimation methods (with parameter θ) for these models are proposed. The solutions of both methods are proved to be mathematically equivalent although the principles are different. Another method based on the well-known likelihood function is applied to our model with the parameter, which can expand the application limit of the conventional likelihood method. Various examples are solved by these proposed methods and we have successful results for all.