生成，歧视性的渐近分析，和pseudolikelihood估计][An Asymptotic Analysis of Generative, Discriminative, and Pseudolikelihood Estimators]

生成，歧视性的渐近分析，和pseudolikelihood估计 An Asymptotic Analysis of Generative, Discriminative, and Pseudolikelihood Estimators


课程网址:	http://videolectures.net/icml08_liang_aagdpe/
主讲教师:	Percy Liang
开课单位:	斯坦福大学
开课时间:	2008-07-24
课程语种:	英语
中文简介:	统计和计算问题已经基于各种形式的可能性（例如，联合，条件和伪似然）激励参数估计器。在本文中，我们提出了一个统一的框架来研究这些估计，这使我们可以比较它们的相对（统计）效率。我们的渐近分析表明，对更多数据建模往往会减少方差，但代价是对模型错误指定更敏感。我们提出验证我们分析的实验。
课程简介:	Statistical and computational concerns have motivated parameter estimators based on various forms of likelihood, e.g., joint, conditional, and pseudolikelihood. In this paper, we present a unified framework for studying these estimators, which allows us to compare their relative (statistical) efficiencies. Our asymptotic analysis suggests that modeling more of the data tends to reduce variance, but at the cost of being more sensitive to model misspecification. We present experiments validating our analysis.
关键词:	相对效率; 参数估计; 实验验证; 统计和计算问题
课程来源:	视频讲座网
最后编审:	2020-06-12：邬启凡(课程编辑志愿者)
阅读次数:	63

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