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Found 10 papers, 3 papers with code
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 of some illness, one of the main problems we can encounter is evaluating whether or not said patients are going to require intensive care in the near future.
Context-specific kernel-based hidden Markov model for time series analysis
Traditional hidden Markov models have been a useful tool to understand and model stochastic dynamic data; in the case of non-Gaussian data, models such as mixture of Gaussian hidden Markov models can be used.
Quantum Approximate Optimization Algorithm for Bayesian network structure learning
The approach was applied to a cancer benchmark problem, and the results justified the use of variational quantum algorithms for solving the Bayesian network structure learning problem.
Semiparametric Bayesian Networks
In addition, we present modifications of two well-known algorithms (greedy hill-climbing and PC) to learn the structure of a semiparametric Bayesian network from data.
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models
In a real life process evolving over time, the relationship between its relevant variables may change.
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.
Markov Property in Generative Classifiers
We show that, for generative classifiers, conditional independence corresponds to linear constraints for the induced discrimination functions.
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.
