We extend AMP chain graphs by (i) relaxing the semidirected acyclity constraint so that only directed cycles are forbidden, and (ii) allowing up to two edges between any pair of nodes. We introduce global, ordered local and pairwise Markov properties for the new models. We show the equivalence of these properties for strictly positive probability distributions. We also show that, when the random variables are normally distributed, the new models can be interpreted as systems of linear equations with correlated errors. Finally, we describe an exact algorithm for learning the new models via answer set programming.
from cs.AI updates on arXiv.org http://ift.tt/1OfZpCV
via IFTTT
No comments:
Post a Comment