Zobrazeno 1 - 10
of 112
pro vyhledávání: '"Leonelli, Manuele"'
Bayesian networks are one of the most widely used classes of probabilistic models for risk management and decision support because of their interpretability and flexibility in including heterogeneous pieces of information. In any applied modelling, i
Externí odkaz:
http://arxiv.org/abs/2407.04667
Traditionally, the sensitivity analysis of a Bayesian network studies the impact of individually modifying the entries of its conditional probability tables in a one-at-a-time (OAT) fashion. However, this approach fails to give a comprehensive accoun
Externí odkaz:
http://arxiv.org/abs/2406.05764
Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of its vertices. Several structural learning routines have been defined and implemented to learn staged trees from data, un
Externí odkaz:
http://arxiv.org/abs/2405.18306
Autor:
Leonelli, Manuele, Varando, Gherardo
Supervised classification is one of the most ubiquitous tasks in machine learning. Generative classifiers based on Bayesian networks are often used because of their interpretability and competitive accuracy. The widely used naive and TAN classifiers
Externí odkaz:
http://arxiv.org/abs/2405.18298
Australian Rules Football is a field invasion game where two teams attempt to score the highest points to win. Complex machine learning algorithms have been developed to predict match outcomes post-game, but their lack of interpretability hampers an
Externí odkaz:
http://arxiv.org/abs/2405.12588
In this paper we investigate the use of staged tree models for discrete longitudinal data. Staged trees are a type of probabilistic graphical model for finite sample space processes. They are a natural fit for longitudinal data because a temporal ord
Externí odkaz:
http://arxiv.org/abs/2401.04297
Autor:
Leonelli, Manuele, Varando, Gherardo
Staged trees are a relatively recent class of probabilistic graphical models that extend Bayesian networks to formally and graphically account for non-symmetric patterns of dependence. Machine learning algorithms to learn them from data have been imp
Externí odkaz:
http://arxiv.org/abs/2401.01812
Autor:
Filigheddu, Maria Teresa, Leonelli, Manuele, Varando, Gherardo, Gómez-Bermejo, Miguel Ángel, Ventura-Díaz, Sofía, Gorospe, Luis, Fortún, Jesús
Machine learning models are increasingly used in the medical domain to study the association between risk factors and diseases to support practitioners in predicting health outcomes. In this paper, we showcase the use of machine-learned staged tree m
Externí odkaz:
http://arxiv.org/abs/2307.16301
Autor:
Crimaldi, Fabio, Leonelli, Manuele
This study explores the concept of creativity and artificial intelligence (AI) and their recent integration. While AI has traditionally been perceived as incapable of generating new ideas or creating art, the development of more sophisticated AI mode
Externí odkaz:
http://arxiv.org/abs/2306.01795
Sensitivity analysis measures the influence of a Bayesian network's parameters on a quantity of interest defined by the network, such as the probability of a variable taking a specific value. Various sensitivity measures have been defined to quantify
Externí odkaz:
http://arxiv.org/abs/2302.00364