Parameter Estimation in Large Causal Models

The assessment of a probability distribution that is associated with a Bayesian network is a challenging task, even if its topology is sparse. Special probability distributions, based on the notion of causal independence, have therefore been proposed, as these allow defining a probability distribution in terms of Boolean combinations of local distributions. In Bayesian networks which need to model a large number of interactions among causal mechanisms even this approach becomes infeasible. We investigate the use of equivalence classes of binomial distributions as a means to define such very large Bayesian networks.