  Bayesian networks (BNs) are widely used graphical models usable to draw
statistical inference about Directed acyclic graphs (DAGs). We presented here
Graph_sampler a fast free C language software for structural inference on BNs.
Graph_sampler uses a fully Bayesian approach in which the marginal likelihood
of the data and prior information about the network structure are considered.
This new software can handle both the continuous as well discrete data and
based on the data type two different models are formulated. The software also
provides a wide variety of structure priors which can be informative or
uninformative. We proposed a new and much faster jumping kernel strategy in the
Metropolis-Hastings algorithm. The source C code distributed is very compact,
fast, uses low memory and disk storage. We performed out several analyses based
on different simulated data sets and synthetic as well as real networks to
discuss the performance of Graph_sampler.
