In stochastic claims reserving, state space models have been used for almost 40 years to forecast loss reserves and to compute their mean squared error of prediction. Although state space models and the associated Kalman filter learning algorithms are very powerful and flexible tools, comparatively few articles on this topic were published during this period. Most recently, several articles have been published which highlight the benefits of state space models in stochastic claims reserving and may lead to a significant increase in its popularity for applications in actuarial practice. To further emphasize the merits of these papers, this commentary highlights various additional aspects that are useful for practical applications and offer some fruitful directions for future research.

中文翻译：

---

卡尔曼滤波器学习算法和随机声明保留的状态空间表示

在随机索赔准备金中，近 40 年来一直使用状态空间模型来预测损失准备金并计算其预测的均方误差。尽管状态空间模型和相关的卡尔曼滤波器学习算法是非常强大和灵活的工具，但在此期间发表的关于该主题的文章相对较少。最近，发表了几篇文章，强调了状态空间模型在随机索赔准备金中的好处，并可能导致其在精算实践中的应用显着增加。为了进一步强调这些论文的优点，本评论强调了对实际应用有用的各种其他方面，并为未来的研究提供了一些富有成效的方向。