Approximating Joint Probability Distributions Given Partial Information

In this paper, we propose new methods to approximate probability distributions that are incompletely specified. We compare these methods to the use of maximum entropy and quantify the accuracy of all methods within the context of an illustrative example. We show that within the context of our example, the methods we propose are more accurate than existing methods.