Maximum Likelihood Estimation Under Order Restrictions by the Prior Feedback Method
提出一种通用优化方法，通过序列形式贝叶斯估计（方差趋于零）来求解顺序约束下的最大似然估计，适用于精确计算或MCMC近似，并用两个实例演示。
Abstract Algorithms for deriving isotonic regression estimators in order-restricted linear models and more generally restricted maximum likelihood estimators are usually quite dependent on the particular problem considered. We propose here an optimization method based on a sequence of formal Bayes estimates whose variances converge to zero. This method, akin to simulated annealing, can be applied “universally”; that is, as long as these Bayes estimators can be derived by exact computation or Markov chain Monte Carlo sampling approximation. We then give an illustration of our method for two real-life examples.