  Iterative Proportional Fitting (IPF), combined with EM, is commonly used as
an algorithm for likelihood maximization in undirected graphical models. In
this paper, we present two iterative algorithms that generalize upon IPF. The
first one is for likelihood maximization in discrete chain factor graphs, which
we define as a wide class of discrete variable models including undirected
graphical models and Bayesian networks, but also chain graphs and sigmoid
belief networks. The second one is for conditional likelihood maximization in
standard undirected models and Bayesian networks. In both algorithms, the
iteration steps are expressed in closed form. Numerical simulations show that
the algorithms are competitive with state of the art methods.
