We emphasize that im produces marginal, not joint posteriors, so that failure of one parameter to converge is no reflection of uncertain inference for other parameters.
Thus the emphasis has shifted, at least for the time being, to viewing NLP as a problem of uncertain inference and learning in a stochastic setting.
The researching results are applied in the designed of uncertain inference engine in fuzzy expert system.
In addition, Liu [14] presented the concepts of uncertain system and uncertain inference controller.
In any model of partial belief and uncertain inference, there are some things about which you are uncertain.
In order to derive consequences from human knowledge, Liu [14] proposed uncertain inference via uncertain set theory.
As an application, Gao [17] balanced an inverted pendulum by using the uncertain inference control.
So far, it has brought about many branches such as uncertain programming (Liu [22]), uncertain risk analysis (Liu [1]), uncertain process (Liu [23]), uncertain differential equation (Liu [23], Yao [24]), uncertain logic (Liu [18]), uncertain finance (Liu [25], Yao [26]), and uncertain inference control (Liu [14], Gao [17]).
These techniques include aspects of NLU, ML, goal-directed and uncertain inference, ontologies, planning, and service delegation.
