论文标题
模型选择和假设测试的边际可能性计算：广泛的审查
Marginal likelihood computation for model selection and hypothesis testing: an extensive review
论文作者
论文摘要
这是用于模型选择和假设检验的边际似然计算的最新介绍，概述。计算概率模型（或常数比率）的标准化常数是统计，应用数学，信号处理和机器学习的许多应用中的基本问题。本文对该主题的最新研究进行了全面研究。我们重点介绍了不同技术之间的限制，收益，联系和差异。还描述了使用不当先验的问题和可能的解决方案。通过理论比较和数值实验比较了一些最相关的方法。
This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing. Computing normalizing constants of probability models (or ratio of constants) is a fundamental issue in many applications in statistics, applied mathematics, signal processing and machine learning. This article provides a comprehensive study of the state-of-the-art of the topic. We highlight limitations, benefits, connections and differences among the different techniques. Problems and possible solutions with the use of improper priors are also described. Some of the most relevant methodologies are compared through theoretical comparisons and numerical experiments.
