  We apply the principle of maximum entropy to select a unique joint
probability distribution from the set of all joint probability distributions
specified by a credal network. In detail, we start by showing that the unique
joint distribution of a Bayesian tree coincides with the maximum entropy model
of its conditional distributions. This result, however, does not hold anymore
for general Bayesian networks. We thus present a new kind of maximum entropy
models, which are computed sequentially. We then show that for all general
Bayesian networks, the sequential maximum entropy model coincides with the
unique joint distribution. Moreover, we apply the new principle of sequential
maximum entropy to interval Bayesian networks and more generally to credal
networks. We especially show that this application is equivalent to a number of
small local entropy maximizations.
