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03/10/2023
Classifying the evolution of COVID-19 severity on patients with combined dynamic Bayesian networks and neural networks
When we face patients arriving to a hospital suffering from the effects ...
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01/24/2023
Context-specific kernel-based hidden Markov model for time series analysis
Traditional hidden Markov models have been a useful tool to understand a...
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03/04/2022
Quantum Approximate Optimization Algorithm for Bayesian network structure learning
Bayesian network structure learning is an NP-hard problem that has been ...
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09/07/2021
Semiparametric Bayesian Networks
We introduce semiparametric Bayesian networks that combine parametric an...
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10/27/2020
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models
In a real life process evolving over time, the relationship between its ...
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06/02/2020
Sparse Cholesky covariance parametrization for recovering latent structure in ordered data
The sparse Cholesky parametrization of the inverse covariance matrix can...
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09/03/2019
Generating random Gaussian graphical models
Structure learning methods for covariance and concentration graphs are o...
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12/01/2018
Towards Gaussian Bayesian Network Fusion
Data sets are growing in complexity thanks to the increasing facilities ...
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11/12/2018
Markov Property in Generative Classifiers
We show that, for generative classifiers, conditional independence corre...
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09/02/2018
A fast Metropolis-Hastings method for generating random correlation matrices
We propose a novel Metropolis-Hastings algorithm to sample uniformly fro...
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07/09/2018
A partial orthogonalization method for simulating covariance and concentration graph matrices
Structure learning methods for covariance and concentration graphs are o...
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06/28/2018
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
The PC algorithm is a popular method for learning the structure of Gauss...
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06/23/2016
On Gaussian Markov models for conditional independence
Markov models lie at the interface between statistical independence in a...
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01/16/2013
