no code implementations • 28 May 2024 • Manuele Leonelli, Gherardo Varando
Generative classifiers based on Bayesian networks are often used because of their interpretability and competitive accuracy.
no code implementations • 28 May 2024 • Jack Storror Carter, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of its vertices.
no code implementations • 1 Jun 2023 • Fabio Crimaldi, Manuele Leonelli
This study explores the concept of creativity and artificial intelligence (AI) and their recent integration.
no code implementations • 1 Feb 2023 • Rafael Ballester-Ripoll, Manuele Leonelli
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.
no code implementations • 2 Jan 2023 • Manuele Leonelli, Gherardo Varando
Bayesian networks are widely used to learn and reason about the dependence structure of discrete variables.
1 code implementation • 17 Jun 2022 • Rafael Ballester-Ripoll, Manuele Leonelli
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.
no code implementations • 14 Jun 2022 • Manuele Leonelli, Gherardo Varando
Several structural learning algorithms for staged tree models, an asymmetric extension of Bayesian networks, have been defined.
no code implementations • 8 Mar 2022 • Manuele Leonelli, Gherardo Varando
Bayesian networks faithfully represent the symmetric conditional independences existing between the components of a random vector.
no code implementations • 7 Oct 2021 • Rafael Ballester-Ripoll, Manuele Leonelli
We show how to apply Sobol's method of global sensitivity analysis to measure the influence exerted by a set of nodes' evidence on a quantity of interest expressed by a Bayesian network.
no code implementations • 4 Aug 2021 • Gherardo Varando, Federico Carli, Manuele Leonelli
Bayesian networks are a widely-used class of probabilistic graphical models capable of representing symmetric conditional independence between variables of interest using the topology of the underlying graph.
no code implementations • 25 Jul 2021 • Manuele Leonelli, Ramsiya Ramanathan, Rachel L. Wilkerson
Bayesian networks are a class of models that are widely used for risk assessment of complex operational systems.
no code implementations • 8 Jun 2021 • Manuele Leonelli, Gherardo Varando
Causal discovery algorithms aim at untangling complex causal relationships from data.
no code implementations • 26 Dec 2020 • Federico Carli, Manuele Leonelli, Gherardo Varando
Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule.
no code implementations • 29 Oct 2020 • Christiane Görgen, Manuele Leonelli, Orlando Marigliano
Staged tree models are a discrete generalization of Bayesian networks.
Statistics Theory Methodology Statistics Theory
1 code implementation • 14 Apr 2020 • Federico Carli, Manuele Leonelli, Eva Riccomagno, Gherardo Varando
stagedtrees is an R package which includes several algorithms for learning the structure of staged trees and chain event graphs from data.
no code implementations • 18 Dec 2018 • Manuele Leonelli, Eva Riccomagno
Sensitivity analysis in probabilistic discrete graphical models is usually conducted by varying one probability value at a time and observing how this affects output probabilities of interest.
no code implementations • 27 Sep 2018 • Christiane Goergen, Manuele Leonelli
However, for Gaussian graphical models, such variations usually make the original graph an incoherent representation of the model's conditional independence structure.
no code implementations • 2 Aug 2016 • Manuele Leonelli, Jim Q. Smith
We then proceed with the construction of a directed expected utility network to support decision makers in the domain of household food security.
no code implementations • 28 Jul 2016 • Manuele Leonelli, Eva Riccomagno, Jim Q. Smith
For problems where all random variables and decision spaces are finite and discrete, here we develop a symbolic way to calculate the expected utilities of influence diagrams that does not require a full numerical representation.
no code implementations • 7 Dec 2015 • Manuele Leonelli, Christiane Görgen, Jim Q. Smith
Sensitivity methods for the analysis of the outputs of discrete Bayesian networks have been extensively studied and implemented in different software packages.