Bayesian Inference for Directed Acyclic Graphs

Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian networks. References: J. Kuipers, P. Suter and G. Moffa (2018) , N. Friedman and D. Koller (2003) , D. Geiger and D. Heckerman (2002) , J. Kuipers and G. Moffa (2017) , M. Kalisch et al.(2012) .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


2.0.4 by Polina Suter, 2 months ago

Browse source code at

Authors: Polina Suter [aut, cre] , Jack Kuipers [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, methods, graph, Rgraphviz, RBGL, pcalg, graphics

Linking to Rcpp

Imported by Bestie.

See at CRAN