Probabilistic Measures of Adequacy of a Numerical Search for a Global Maximum
该文提出用未观测物种概率度量来评估随机起点搜索全局最大值的充分性，估计未观测全局最大值的概率，并用于混合正态模型的最大似然估计搜索。
Abstract Measures of the probability of all unobserved species are applied to the problem of assessing the adequacy of a search for a global maximum using random starting points. The measures, as used here, estimate the probability that an iterative algorithm using a randomly selected starting point will find a solution not observed in previous random starting points. The probability of an unobserved global maximum is less than or equal to this probability. We used these measures to evaluate the adequacy of our search procedure for the maximum likelihood estimates of the parameters of a mixture of two normals. These measures indicated that for most problems generated there was little chance that there were unobserved domains of convergence. Occasional problems, however, had appreciable estimated probabilities. In such problems, examination of the data suggested regions where a more focused search for unobserved domains of convergence was warranted.