Bayesian estimation of proportions with a cross-entropy prior

This paper suggests estimators of the frequencies (N₉) or proportions (N_e/N) of N distinguishable objects contained in S categories, given various types of information. We consider information in the form of exact constraints on the N_$)sample frequencies, and frequencies of related data. The analysis uses Bayesian methods, where the prior distribution is assumed to be a function of the cross-entropy between the N_sand a reference distribution. We show the relationship between our estimator and the log-linear and logit models and also present a sampling experiment to compare our proposed estimator with the iterated proportional fitting estimator.