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Found 4 papers, 0 papers with code
Sparse Cholesky covariance parametrization for recovering latent structure in ordered data
The sparse Cholesky parametrization of the inverse covariance matrix can be interpreted as a Gaussian Bayesian network; however its counterpart, the covariance Cholesky factor, has received, with few notable exceptions, little attention so far, despite having a natural interpretation as a hidden variable model for ordered signal data.
Towards Gaussian Bayesian Network Fusion
In this paper we propose a method for the aggregation of different Bayesian network structures that have been learned from separate data sets, as a first step towards mining data sets that need to be partitioned in an horizontal way, i. e. with respect to the instances, in order to be processed.
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks
We show that the parameters found by a BO method outperform those found by a random search strategy and the expert recommendation.
A review of Gaussian Markov models for conditional independence
Markov models lie at the interface between statistical independence in a probability distribution and graph separation properties.
