Evidential reasoning using stochastic simulation of causal models
Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Non-additive measures by interval probability functions
Information Sciences—Informatics and Computer Science: An International Journal
Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks
International Journal of Approximate Reasoning
Graphical models for imprecise probabilities
International Journal of Approximate Reasoning
IEEE Transactions on Fuzzy Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Fuzzy rule interpolation based on the ratio of fuzziness of interval type-2 fuzzy sets
Expert Systems with Applications: An International Journal
Self-adaptive interval type-2 neural fuzzy network control for PMLSM drives
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
It is necessary and challenging to represent the probabilities of fuzzy events and make inferences between them based on a Bayesian network. Motivated by such real applications, in this paper, we first define the interval probabilities of type-2 fuzzy events. Then, we define weak interval conditional probabilities and the corresponding probabilistic description. The expanded multiplication rule supporting interval probability reasoning. Accordingly, we propose the approach for learning the interval conditional probability parameters of a Bayesian network and the algorithm for its approximate inference. Experimental results show the feasibility of our method.