论文标题
马尔可夫风险映射和风险敏感的最佳预测
Markov risk mappings and risk-sensitive optimal prediction
论文作者
论文摘要
我们在动态风险框架下以最少的假设制定了离散时间的概率马尔可夫属性。这对于动态优化问题的风险敏感版本（例如最佳预测）的递归解决方案很有用，在每个阶段，递归取决于整个未来。该属性符合实践中使用的标准风险度量，并以几种等效版本制定，包括通过接受集，强烈版本和双重表示形式的表示形式。
We formulate a probabilistic Markov property in discrete time under a dynamic risk framework with minimal assumptions. This is useful for recursive solutions to risk-sensitive versions of dynamic optimisation problems such as optimal prediction, where at each stage the recursion depends on the whole future. The property holds for standard measures of risk used in practice, and is formulated in several equivalent versions including a representation via acceptance sets, a strong version, and a dual representation.
