
A new class of generative classifiers based on staged tree models
Generative models for classification use the joint probability distribut...
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Learning DAGs without imposing acyclicity
We explore if it is possible to learn a directed acyclic graph (DAG) fro...
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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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Graphical continuous Lyapunov models
The linear Lyapunov equation of a covariance matrix parametrizes the equ...
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The R Package stagedtrees for Structural Learning of Stratified Staged Trees
stagedtrees is an R package which includes several algorithms for learni...
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Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small pvalues
In this article, we describe the algorithms for causal structure learnin...
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Generating random Gaussian graphical models
Structure learning methods for covariance and concentration graphs are o...
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Markov Property in Generative Classifiers
We show that, for generative classifiers, conditional independence corre...
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A fast MetropolisHastings method for generating random correlation matrices
We propose a novel MetropolisHastings algorithm to sample uniformly fro...
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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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Gherardo Varando
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