Performance evaluation based on maximum entropy Markov model

This paper presents a new performance evaluation method based on the maximum entropy Markov model, which quantifies the process of scoring as a Markov process, represents the scoring rules by characteristic functions and obtains the optimal model parameters by maximizing the maximum entropy over a training sample set. Compared with other traditional methods, this method has the ability to combine complex scoring rules, expert experience with logical connection of the evaluated items to get comprehensive evaluation results. To improve the efficiency of training and scoring, this paper adopts the improved iterative scaling algorithm to obtain near-optimal model parameters and uses the Viterbi algorithm to quickly calculate the final evaluation results. The proposed method has been applied in the history data of China Ocean Mineral Resources R&D Association's evaluation system for simulation. The experimental results show that this method has higher accuracy compared with BP networks and the classical maximum entropy model.