论文标题
模板拟合的快速，稳定的近似最大样本方法
A fast and stable approximate maximum-likelihood method for template fits
论文作者
论文摘要
巴洛（Barlow）和比斯顿（Beeston）提出了拟合复合模型的问题，该复合模型由从蒙特 - 卡洛模拟获得的集合模板组成，这些模板适合同样的数据。解决确切的可能性在技术上具有挑战性，因此康威提出了解决这些挑战的可能性。在本文中，新的近似可能性源自确切的Barlow-Beeston。新的近似可能性和Conway的可能性被推广到将加权数据与加权模板拟合的问题。在两个玩具示例中研究了所有三个可能性获得的估计值：一个简单的示例和一个具有挑战性的估计。近似可能性的性能与确切的Barlow-beeston可能性相当，而与加权模板的拟合性能更好。当垃圾箱数量较大时，大概的可能性比Barlow-beeston评估速度更快。
Barlow and Beeston presented an exact likelihood for the problem of fitting a composite model consisting of binned templates obtained from Monte-Carlo simulation which are fitted to equally binned data. Solving the exact likelihood is technically challenging, and therefore Conway proposed an approximate likelihood to address these challenges. In this paper, a new approximate likelihood is derived from the exact Barlow-Beeston one. The new approximate likelihood and Conway's likelihood are generalized to problems of fitting weighted data with weighted templates. The performance of estimates obtained with all three likelihoods is studied on two toy examples: a simple one and a challenging one. The performance of the approximate likelihoods is comparable to the exact Barlow-Beeston likelihood, while the performance in fits with weighted templates is better. The approximate likelihoods evaluate faster than the Barlow-Beeston one when the number of bins is large.
