MMGET: A Markov model for generalized evidence theory
  In real life, lots of information merges from time to time. To appropriately
describe the actual situations, lots of theories have been proposed. Among
them, Dempster-Shafer evidence theory is a very useful tool in managing
uncertain information. To better adapt to complex situations of open world, a
generalized evidence theory is designed. However, everything occurs in sequence
and owns some underlying relationships with each other. In order to further
embody the details of information and better conforms to situations of real
world, a Markov model is introduced into the generalized evidence theory which
helps extract complete information volume from evidence provided. Besides, some
numerical examples is offered to verify the correctness and rationality of the
proposed method.
