On expressiveness of the chain graph interpretations
We study the expressiveness of the different chain graph interpretations in terms of representable independence models. We show how an MCMC sampling algorithm can be used to sample chain graph models. We show that chain graphs can represent exponentially many more independence models compared to Bayesian networks. We show (numerically) that the average number of chain graphs per chain graph model appears to converge. We show that AMP and MVR chain graphs are more expressive than LWF chain graphs.
chain graphs mcmc sampling lauritzenwermuthfrydenberg interpretation anderssonmadiganperlman interpretation multivariate regression interpretation expressibility of probabilistic graphical models