Estimation of Discrete Distributions with a Class of Simplex Constraints
研究了在单调性、凸性等单纯形约束下，利用EM算法和数据增广算法对二项、泊松等离散分布进行最大似然和贝叶斯估计，并通过实例展示方法应用。
Abstract Simplex constraints, such as monotonicity and convexity or concavity on the probabilities of a set of discrete distributions, are useful for modeling and analyzing discrete data. This article considers both maximum likelihood estimation and Bayesian estimation of discrete distribution with a class of simplex constraints using the Expectation-Maximization (EM) algorithm and the data augmentation (DA) algorithm. The formulation and implementation of EM and DA for binomial, Poisson, hierarchical Poisson-binomial, multinomial, and hierarchical multinomial distributions are considered in detail and illustrated with examples.