Gaussian Mixture Graphical Model Learning and Inference
Sample a Gaussian mixture model
Select the number of mixture components and estimate the parameters of...
Perform smoothing inference in a Gaussian mixture dynamic Bayesian net...
Split a mixture component of a Gaussian mixture model
Select the explanatory variables, the number of mixture components and...
Learn the structure and the parameters of a Gaussian mixture graphical...
Learn the structure and the parameters of a Gaussian mixture graphical...
Provide the graphical structure of a Gaussian mixture graphical model
Summarize a Gaussian mixture model or graphical model
Add arcs to a Gaussian mixture graphical model
Add nodes to a Gaussian mixture graphical model
Add variables to a Gaussian mixture model
Aggregate particles to obtain inferred values
Compute the Akaike Information Criterion (AIC) of a Gaussian mixture m...
Perform inference in a Gaussian mixture Bayesian network
Compute the log-likelihood of a Gaussian mixture model or graphical mo...
Merge mixture components of a Gaussian mixture model
Display the graphical structure of a Gaussian mixture Bayesian network
Learn the parameters of a Gaussian mixture graphical model with incomp...
Learn the parameters of a Gaussian mixture graphical model
Initialize particles to perform inference in a Gaussian mixture graphi...
Perform predictive inference in a Gaussian mixture dynamic Bayesian ne...
Propagate particles forward in time
Compute the Bayesian Information Criterion (BIC) of a Gaussian mixture...
Conditionalize a Gaussian mixture model
Compute densities of a Gaussian mixture model
Create a Gaussian mixture Bayesian network
Display the mixture components of a Gaussian mixture model
Estimate the parameters of a Gaussian mixture model
Compute expectations of a Gaussian mixture model
Perform filtering inference in a Gaussian mixture dynamic Bayesian net...
Create a Gaussian mixture dynamic Bayesian network
Gaussian mixture graphical model learning and inference
Create a Gaussian mixture model
Extract the minimal sub-Gaussian mixture graphical model required to i...
Remove arcs from a Gaussian mixture graphical model
Remove nodes from a Gaussian mixture graphical model
Remove variables from a Gaussian mixture model
Rename nodes of a Gaussian mixture graphical model
Rename variables of a Gaussian mixture model
Reorder the variables and the mixture components of a Gaussian mixture...
Gaussian mixture graphical models include Bayesian networks and dynamic Bayesian networks (their temporal extension) whose local probability distributions are described by Gaussian mixture models. They are powerful tools for graphically and quantitatively representing nonlinear dependencies between continuous variables. This package provides a complete framework to create, manipulate, learn the structure and the parameters, and perform inference in these models. Most of the algorithms are described in the PhD thesis of Roos (2018) <https://tel.archives-ouvertes.fr/tel-01943718>.