Alessandro Antonucci

TL;DR: In this paper, a message-passing algorithm based on epistemic irrelevance was proposed to compute updated beliefs for a variable in the tree, which is formulated entirely in terms of coherent lower previsions, and satisfies a number of rationality requirements.

TL;DR: Credal classifiers are a novel class of imprecise probabilistic graphical models, called credal networks, which allow for indecision between two or more classes, providing a less informative but more robust conclusion than Bayesian classifiers.

TL;DR: Two credal classifiers for multivariate time series based on imprecise HMMs, one based on the expected value of the mixture, the other on the Bhattacharyya distance between pairs of mixtures are developed.

TL;DR: This paper delivers a new graphical language to formulate any type of credal network, both separately and non-separately specified, and shows that any non- separating net represented with the new language can be easily transformed into an equivalent separately specified net, defined over a larger domain.

TL;DR: It is shown that inferences under strong independence are NP-hard even in trees with binary variables except for a single ternary one, and it is proved that under epistemic irrelevance the polynomial-time complexity of inferences in credal trees is not likely to extend to more general models.