Logic of probability entailment with threshold-surety

We offer a logic of probability entailment with threshold-surety approach to a probabilistic logic, which treats a finite set of premises in a knowledge base as assertions that are true with probability at least some primary threshold, and consider as consequences those assertions probabilistically entailed to have probability at least some suitable threshold. We use the concept of fuzzy c-partition and the maximum fuzzy entropy principle to select the threshold values for knowledge base and conduct an illustrative experiment.