Description: A general framework to perform Bayesian structure learning in undirected graphical models. The main target is high-dimensional data analysis wherein either continuous or discrete variables.

Title: Bayesian Graph Selection Based on Birth-Death MCMC Approach

Title: Bayesian undirected graph estimation with BD-MCMC
Packaged: 2013-03-24 12:21:52 UTC; Administrator

Description: Bayesian inference for structure learning in undirected graphical models. The main target is to uncover complicated patterns in multivariate data wherein either continuous or discrete variables.

Description: Provides statistical tools for Bayesian structure learning in undirected graphical models with either continuous or discrete variables. The main target is to uncover complicated patterns in multivariate data wherein either continuous or discrete variables.

Description: Provides statistical tools for Bayesian structure learning in undirected graphical models with either continuous or discrete variables.

Description: Provides statistical tools for Bayesian structure learning in undirected graphical models with both continuous and discrete variables.

Title: Bayesian Structure Learning in Graphical Models using

Title: Bayesian undirected graph estimation with BDMCMC algorithm

Description: Provides statistical tools for Bayesian structure learning in undirected graphical models with both continuous and discrete variables. The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889> and Mohammadi et al. (2017) <doi:10.1111/rssc.12171>.

Description: Provides statistical tools for Bayesian structure learning in undirected graphical models for continuous, discrete, and mixed data. The package is implemented the recent improvements in the Bayesian graphical models literature, including Mohammadi and Wit (2015) <doi:10.1214/14-BA889> and Mohammadi et al. (2017) <doi:10.1111/rssc.12171>. To speed up the computations, the BDMCMC sampling algorithms are implemented in parallel using OpenMP in C++.