Description 
In this project, two algorithms related to socalled local monotonicity
in probabilistic networks are implemented, tested, and benchmarked. The
algorithms compute:
 whether the values of the variables in a probabilistic network can be
ordered in such a way that the network as a whole is locally monotone in
distribution;
 an optimal ordering that maximizes the number of monotone arcs in
the network.
The first algorithm consists of a preprocessing phase (to minimize the
search space) and, when needed, a branching phase (to find a solution).
The second algorithm will be a branchandbound approach that leans
heavily on (a variant of) the first algorithm to calculate lower bounds.
The algorithms will be tested on a test set of probabilistic networks
that are often used for research purposes.
The algorithms are implemented in Java.
References:
 Kwisthout, J., Bodlaender, H.L., and Tel, G. Complexity Results for
Local Monotonicity in Probabilistic Networks, UU technical report, in
preperation.
 Van der Gaag, L.C., Bodlaender, H.L. and Feelders, A. Monotonicity in
Bayesian Networks, Proceedings of the UAI, 2004.
 Van der Gaag, L.C., Renooij, S., and Geenen, P.L. Lattices for
Studying Monotonicity of Bayesian Networks. Proceedings of PGM'06, 2006.
