论文标题
隐藏的马尔可夫模型中不确定性的强大过滤和传播
Robust Filtering and Propagation of Uncertainty in Hidden Markov Models
论文作者
论文摘要
我们考虑对连续时间有限态隐藏的马尔可夫模型的过滤，其中速率和观察矩阵取决于未知的时间依赖性参数，为此，没有先验或随机模型可用。当我们收集新的观察结果时，我们量化和分析了如何在时间上传播诱导的不确定性，并通过基于路径过滤和对粗糙微分方程的最佳控制的新结果来同时提供对隐藏信号的强大估计并学习未知参数。
We consider the filtering of continuous-time finite-state hidden Markov models, where the rate and observation matrices depend on unknown time-dependent parameters, for which no prior or stochastic model is available. We quantify and analyze how the induced uncertainty may be propagated through time as we collect new observations, and used to simultaneously provide robust estimates of the hidden signal and to learn the unknown parameters, via techniques based on pathwise filtering and new results on the optimal control of rough differential equations.
