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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Li, A., Lio, W. Bayesian inference in the framework of uncertainty theory. J Ambient Intell Human Comput (2024). https://doi.org/10.1007/s12652-024-04785-z
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DOI: https://doi.org/10.1007/s12652-024-04785-z