Naive Bayes models have been widely used for clustering and classification. However, they are seldom used for general probabilistic learning and inference (i.e., for estimating an...
We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic graphs. We show that the intractability of exact inference in such networks do...
—Adapting the hyperparameters of support vector machines (SVMs) is a challenging model selection problem, especially when flexible kernels are to be adapted and data are scarce....
Abstract. We develop an algorithm to compute timed reachability probabilities for distributed models which are both probabilistic and nondeterministic. To obtain realistic results ...
Georgel Calin, Pepijn Crouzen, Pedro R. D'Argenio,...