Bayesian Network Parameter Learning Method Based on AHP/D-S Evidence Theory

Abstract

Aiming at the problem of prior knowledge acquisition in the process of Bayesian network construction, AHP/D-S evidence theory is introduced into Bayesian network parameter learning. An algorithm that uses AHP/D-S evidence theory to integrate expert prior knowledge, integrates monotonic constraints and near-equal constraints for parameter learning is proposed, and simulation cases are studied. Given corrective expert prior knowledge, the new parameter-learning algorithm overcomes the shortcomings of miscalculation and miscalculation of certain small probability parameters under the condition of small sample set by MLE, and was obviously better than MLE and MAP without prior information. This paper provides a new method for acquiring prior knowledge in the Bayesian network parameter learning process.