Approximating Tail Areas of Probability Distributions

Approximating discrete probability distributions with Bayesian networks

I generalise the arguments of [Chow & Liu 1968] to show that a Bayesian network satisfying some arbitrary constraint that best approximates a probability distribution is one for which mutual information weight is maximised. I give a practical procedure for finding an approximation network. The plan is first to discuss the approximation problem and its link with Bayesian network theory. After id...

Approximating Joint Probability Distributions Given Partial Information

I n 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.

Approximating discrete probability distributions with decomposable models

A heuristic procedure is presented to approximate an 71dimensional discrete probability distribution with a decomposable model of a given complexity. It is shown that, without loss of generality, the search space can be restricted to a suitable subclass of decomposable models, whose members are called elementary models. The selected elementary model is constructed in an incremental manner accor...

Approximating discrete probability distributions with dependence trees

IEEE Absfracf-A method is presented to approximate optimally an n-dimensional discrete probability distribution by a product of second-order distributions, or the distribution of the first-order tree dependence.-The problem is to find an optimum set of n-1 first order dependence relationship among the n variables. It is shown that the procedure derived in this paper yields an approximation of a...

Approximating Probability Distributions to Reduce Storage Requirements