论文标题
学习跳跃马尔可夫线性系统的各种期望最大化算法
A Variational Expectation-Maximisation Algorithm for Learning Jump Markov Linear Systems
论文作者
论文摘要
跳跃马尔可夫线性系统（JMLS）是一个有用的类，可用于建模在操作过程中表现出行为随机变化的过程。本文提出了一种使用预期最大化（EM）方法来学习跳跃马尔可夫线性系统参数的数值稳定方法。本文提供的解决方案是确定性算法，不是基于蒙特卡洛的技术。结果，模拟表明，与替代方法相比，可以在固定的计算时间内找到一组更可能的系统参数，这可以更好地解释系统的观察结果。
Jump Markov linear systems (JMLS) are a useful class which can be used to model processes which exhibit random changes in behavior during operation. This paper presents a numerically stable method for learning the parameters of jump Markov linear systems using the expectation-maximisation (EM) approach. The solution provided herein is a deterministic algorithm, and is not a Monte Carlo based technique. As a result, simulations show that when compared to alternative approaches, a more likely set of system parameters can be found within a fixed computation time, which better explain the observations of the system.
