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引用次数: 0
摘要
摘要 贝叶斯推理是现代统计学的重要课题之一。在贝叶斯统计中,被视为某种随机变量的参数的信息将由后验分布的信息更新。换句话说,贝叶斯统计中的所有推断都是基于更新的后验信息,这已被证明是一种非常强大的技术。本文以 Lio 和 Kang 于 2022 年提出的不确定贝叶斯规则为基础,研究不确定理论框架下的贝叶斯推断。更准确地说,本文探讨了不确定理论下贝叶斯统计中的点估计、可信区间和假设检验等问题,并给出了我们的方法在智商测试问题中的一个应用。
Bayesian inference in the framework of uncertainty theory
Abstract
Bayesian inference is one of the important topics in modern statistics. The information of the parameter in Bayesian statistics which is regarded as some random variable will be updated by that of the posterior distribution. In other words, all the inferences in Bayesian statistics are based on the updated posterior information, which has been proven to be a very powerful technique. In this paper, we study the Bayesian inference in the framework of uncertainty theory based on the uncertain Bayesian rule developed by Lio and Kang in 2022. To be more precise, issues on the point estimation, credible intervals and hypothesis testing in Bayesian statistics under uncertain theory are explored, and one application of our method in an IQ test problem is also given in this paper.
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