Naive Bayes classiiers can be seen as special probabilistic networks with a star-like structure. They can easily be induced from a dataset of sample cases. However, as most probabilistic approaches, they run into problems, if imprecise (i.e, set-valued) information in the data to learn from has to be taken into account. An approach to handle uncertain as well imprecise information, which recent...

The problem of calculating posterior probabilities for a collection of competing models and associated Bayes factors continues to be a formidable challenge for applied Bayesian statisticians. Current approaches that take advantage of modern Markov chain Monte Carlo (MCMC) computing methods include those that attempt to sample over some form of the joint space created by the model indicators and...

collaborative spectrum sensing (css) is an effective approach to improve the detection performance in cognitive radio (cr) networks. inherent characteristics of the cr have imposed some additional security threats to the networks. one of the common threats is primary user emulation attack (puea). in puea, some malicious users try to imitate primary signal characteristics and defraud the cr user...