标题： 基于信息论的贝叶斯预测 显示英文标题

标题： Bayesian forecasting with information theory

摘要： 对未来实验区分理论或发现自然新定律的能力进行预测的技术，在许多科学领域都非常有趣。 本文介绍了一种基于信息论的贝叶斯预测方法。我们认为在这个背景下互信息是一个合适的量来研究。 除了是贝叶斯的之外，这个提议还有一个优点，即不依赖于基准参数的选择，描述“真实”理论（这是先验未知的），并且适用于任何概率分布。我们证明所提出的方法可用于参数估计和模型选择，这两者都与未来实验有关。我们认为互信息在这两种情况下都有合理的解释。 此外，我们陈述了一些命题，这些命题为一些贝叶斯实践提供了信息论意义，例如执行多个实验、组合不同的数据集以及边缘化。 显示英文摘要

摘要： Forecasting techniques for assessing the power of future experiments to discriminate between theories or discover new laws of nature are of great interest in many areas of science. In this paper, we introduce a Bayesian forecasting method using information theory. We argue that mutual information is a suitable quantity to study in this context. Besides being Bayesian, this proposal has the advantage of not relying on the choice of fiducial parameters, describing the "true" theory (which is a priori unknown), and is applicable to any probability distribution. We demonstrate that the proposed method can be used for parameter estimation and model selection, both of which are of interest concerning future experiments. We argue that mutual information has plausible interpretation in both situations. In addition, we state a number of propositions that offer information-theoretic meaning to some of the Bayesian practices such as performing multiple experiments, combining different datasets, and marginalization.