This paper proposes a novel interpretation of the constant gain learning algorithm through a probabilistic setting with Bayesian updating. The underlying process for the variable being estimated is not specified a priori through a parametric model, and only its probabilistic structure is defined. Such framework allows to understand the gain coefficient in the learning algorithm in terms of the probability of changes in the estimated variable. On the basis of this framework, I assess the range of values commonly used in the macroeconomic empirical literature in terms of the implied probabilities of changes in the estimated variables.

中文翻译：

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恒增益学习算法的概率解释

本文通过贝叶斯更新的概率设置提出了一种恒定增益学习算法的新颖解释。没有通过参数模型先验地指定要估计的变量的基础过程，而仅定义了其概率结构。这种框架允许根据估计变量的变化概率来了解学习算法中的增益系数。在此框架的基础上，我根据估计变量变化的隐含概率，评估了宏观经济经验文献中常用的值范围。