m2graph

square adjacency matrix with elements in <code>{0,1}</code>, representing a graph

(optional) labels of the nodes, <code>c(1:n)</code> are used by default

nodes

This function derives a graph object corresponding to an adjacency matrix

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) <arXiv:1803.07859v2>,
N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>,
D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>,
J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>,
M. Kalisch et al.(2012) <doi:10.18637/jss.v047.i11>.

m2graph: Deriving a graph from an adjacancy matrix

Description

Usage

Arguments

Value

Examples