  Many biological networks include cyclic structures. In such cases, Bayesian
networks (BNs), which must be acyclic, are not sound models for structure
learning. Dynamic BNs can be used but require relatively large time series
data. We discuss an alternative model that embeds cyclic structures within
acyclic BNs, allowing us to still use the factorization property and
informative priors on network structure. We present an implementation in the
linear Gaussian case, where cyclic structures are treated as multivariate
nodes. We use a Markov Chain Monte Carlo algorithm for inference, allowing us
to work with posterior distribution on the space of graphs.
