Gherardo Varando | DeepAI

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∙ 07/30/2023

Using Staged Tree Models for Health Data: Investigating Invasive Fungal Infections by Aspergillus and Other Filamentous Fungi

Machine learning models are increasingly used in the medical domain to s...

0 Maria Teresa Filigheddu, et al. ∙

∙ 05/21/2023

Discovering Causal Relations and Equations from Data

Physics is a field of science that has traditionally used the scientific...

0 Gustau Camps-Valls, et al. ∙

∙ 01/02/2023

Learning and interpreting asymmetry-labeled DAGs: a case study on COVID-19 fear

Bayesian networks are widely used to learn and reason about the dependen...

0 Manuele Leonelli, et al. ∙

∙ 06/14/2022

Highly Efficient Structural Learning of Sparse Staged Trees

Several structural learning algorithms for staged tree models, an asymme...

0 Manuele Leonelli, et al. ∙

∙ 03/08/2022

Structural Learning of Simple Staged Trees

Bayesian networks faithfully represent the symmetric conditional indepen...

0 Manuele Leonelli, et al. ∙

∙ 08/04/2021

Staged trees and asymmetry-labeled DAGs

Bayesian networks are a widely-used class of probabilistic graphical mod...

0 Gherardo Varando, et al. ∙

∙ 06/08/2021

Context-Specific Causal Discovery for Categorical Data Using Staged Trees

Causal discovery algorithms aims at untangling complex causal relationsh...

0 Manuele Leonelli, et al. ∙

∙ 12/26/2020

A new class of generative classifiers based on staged tree models

Generative models for classification use the joint probability distribut...

0 Federico Carli, et al. ∙

∙ 06/04/2020

Learning DAGs without imposing acyclicity

We explore if it is possible to learn a directed acyclic graph (DAG) fro...

0 Gherardo Varando, et al. ∙

∙ 06/02/2020

Sparse Cholesky covariance parametrization for recovering latent structure in ordered data

The sparse Cholesky parametrization of the inverse covariance matrix can...

0 Irene Córdoba, et al. ∙

∙ 05/21/2020

Graphical continuous Lyapunov models

The linear Lyapunov equation of a covariance matrix parametrizes the equ...

0 Gherardo Varando, et al. ∙

∙ 04/14/2020

The R Package stagedtrees for Structural Learning of Stratified Staged Trees

stagedtrees is an R package which includes several algorithms for learni...

0 Federico Carli, et al. ∙

∙ 02/21/2020

Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values

In this article, we describe the algorithms for causal structure learnin...

0 Sebastian Weichwald, et al. ∙

∙ 09/03/2019

Generating random Gaussian graphical models

Structure learning methods for covariance and concentration graphs are o...

0 Irene Córdoba, et al. ∙

∙ 11/12/2018

Markov Property in Generative Classifiers

We show that, for generative classifiers, conditional independence corre...

0 Gherardo Varando, et al. ∙

∙ 09/02/2018

A fast Metropolis-Hastings method for generating random correlation matrices

We propose a novel Metropolis-Hastings algorithm to sample uniformly fro...

0 Irene Córdoba, et al. ∙

∙ 07/09/2018

A partial orthogonalization method for simulating covariance and concentration graph matrices

Structure learning methods for covariance and concentration graphs are o...

0 Irene Córdoba, et al. ∙

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