Estimation and filtering of hidden semi Markov models, event based filters and stochastic control.

隐半马尔可夫模型的估计和过滤、基于事件的过滤器和随机控制。

• 批准号: RGPIN-2015-06084
• 负责人: Elliott, Robert
• 金额: $ 2.19万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613933
• 关键词: Estimation; filtering; hidden; semi; Markov

The central objective of the work will be the derivation of new algorithms for signal processing, in particular the estimation of information hidden in noisy signals. This will build on previous contributions by the applicant. Three directions will be pursued. The first will develop new estimation algorithms for hidden semi Markov models, extending our previous work on hidden Markov models. The second will consider event based filtering problems when the observed process is received only when the hidden signal changes by more than a specified amount or when the signal process crosses certain levels. The third will discuss the optimal control of a noisily observed Markov chain using backward stochastic differential equations. In addition to signal processing, speech processing and other areas, an important application of hidden Markov models has been to genome and protein sequencing. However, when modelling discrete sequences, for example in discrete time or in biological applications, if the hidden process is a Markov chain its (random) occupation time in any state is a geometrically distributed random variable. In many areas of applications, including queuing theory, reliability and maintenance, survival analysis, performance evaluation, biology, DNA analysis, and genome sequencing, it seems more general occupation times should be considered. This leads us to consider semi-Markov models. We shall consider 'hidden' semi-Markov models, that is situations where the semi-Markov chain is not observed directly but modulates a second, observed, process.  We shall develop for these models the results found in our book and previous publications.  In the digital world, continuous-time signals must be sampled. Traditionally, they are sampled uniformly in time. The term 'event-based sampling' can refer to the traditional uniform time step sampling, (sometimes called Riemann sampling), but it usually means that samples are taken in response to a priori defined events, such as when the signal changes by more than a specified amount, (send-on-delta), or when it crosses specified levels, (Lebesgue sampling). The objectives of the work will be to obtain new implementable filters. The theory of event-based sampling is more involved but there are many practical benefits including cheaper sensors, reduced communication costs and less data to process.  The third line of research will use our recent results on backward stochastic differential equations to investigate partially observed stochastic control problems. The adjoint process is described by a backward stochastic differential equation; for partially observed problems this is a backward stochastic partial differential equation. Initially we consider this problem for the control of a partially observed Markov chain where a system of backward stochastic ordinary differential equations will give criteria which determine an optimal control.

这项工作的核心目标是推导新的信号处理算法，特别是对隐藏在噪声信号中的信息的估计，这将建立在申请人之前的贡献的基础上。第一个方向是开发新的估计。隐半马尔可夫模型的算法，扩展了我们之前关于隐马尔可夫模型的工作，第二个将考虑当仅当隐藏信号变化超过指定量或信号过程跨越特定级别时接收到的观察过程时的基于事件的过滤问题。 .第三部分将讨论最优控制使用向后随机微分方程的嘈杂观察马尔可夫链。 除了信号处理、语音处理和其他领域之外，隐马尔可夫模型的一个重要应用是基因组和蛋白质测序。然而，当对离散序列进行建模时，例如在离散时间或生物应用中，如果隐藏过程是一个。马尔可夫链其在任何状态下的（随机）占用时间都是几何分布的随机变量，在许多应用领域，包括排队论、可靠性和维护、生存分析、性能评估、生物学、DNA分析和基因组测序，似乎更是如此。应考虑一般占用时间。我们将考虑“隐藏”半马尔可夫模型，即不直接观察到半马尔可夫链而是调制第二个观察到的过程的情况，我们将为这些模型开发所发现的结果。在我们的书和以前的出版物中。 在数字世界中，连续时间信号必须按时间均匀采样。术语“基于事件的采样”可以指传统的均匀时间步采样（有时称为黎曼采样）。表示响应先验定义的事件而采集样本，例如当信号变化超过指定量时（增量发送），或者当信号超过指定级别时（勒贝格采样工作）。将会获得新的可实现的过滤器，基于事件的采样理论涉及更多，但有许多实际的好处，包括更便宜的传感器、降低的通信成本和更少的数据处理。 第三个研究方向将利用我们最近关于向后随机微分方程的结果来研究部分观察的随机控制问题，伴随过程由向后随机微分方程描述；对于部分观察的问题，我们最初是一个向后随机偏微分方程。考虑这个问题来控制部分观察的马尔可夫链，其中向后随机常微分方程组将给出确定最优控制的标准。