We propose an objective Bayesian algorithm for searching the space of Gaussian directed acyclic graph (DAG) models. The algorithm proposed makes use of moment fractional Bayes factors (MFBF) and thus it is suitable for learning sparse graph. The algorithm is implemented by using Armadillo: an open-source C++ linear algebra library.
|Author||Davide Altomare, Guido Consonni and Luca La Rocca|
|Date of publication||2016-11-23 01:29:18|
|Maintainer||Davide Altomare <firstname.lastname@example.org>|
|License||GPL (>= 2)|
dataHuman: Cell signalling pathway data
dataPub: Publishing productivity data
dataSim100: DAG model with 100 nodes and 100 edges
dataSim200: DAG model with 200 nodes and 100 edges
dataSim50: DAG model with 50 nodes and 100 edges
dataSim6: DAG model with 6 nodes and 5 edges
dataSimHuman: Simulated cell signalling pathway data
FBF_GS: Moment Fractional Bayes Factor Stochastic Search with Global...
FBF_LS: Moment Fractional Bayes Factor Stochastic Search with Local...
FBF_RS: Moment Fractional Bayes Factor Stochastic Search for...