摘要翻译：
与传统模型（如隐马尔可夫模型）使用潜在变量或状态空间表示相比，基于预测状态表示的动力系统模型严格地以可观测量定义。此外，PSR具有有效的无限内存，允许它们对一些基于有限内存的模型无法建模的系统进行建模。到目前为止，PSR模型主要是为具有离散观测的领域开发的。在这里，我们发展了预测线性高斯(PLG)模型，这是一类具有连续观测区域的PSR模型。我们证明PLG模型包含线性动力系统模型（也称为Kalman滤波模型或状态空间模型），而使用的参数较少。我们还介绍了一种从数据中估计PLG参数的算法，并与用于估计Kalman滤波器参数的标准期望最大化(EM)算法进行了对比。我们证明了我们的算法是一个一致的估计过程，并给出了初步的实证结果，表明我们的算法优于EM，尤其是随着模型维数的增加。
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英文标题：
《Predictive Linear-Gaussian Models of Stochastic Dynamical Systems》
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作者：
Matthew Rudary, Satinder Singh, David Wingate
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Models of dynamical systems based on predictive state representations (PSRs) are defined strictly in terms of observable quantities, in contrast with traditional models (such as Hidden Markov Models) that use latent variables or statespace representations. In addition, PSRs have an effectively infinite memory, allowing them to model some systems that finite memory-based models cannot. Thus far, PSR models have primarily been developed for domains with discrete observations. Here, we develop the Predictive Linear-Gaussian (PLG) model, a class of PSR models for domains with continuous observations. We show that PLG models subsume Linear Dynamical System models (also called Kalman filter models or state-space models) while using fewer parameters. We also introduce an algorithm to estimate PLG parameters from data, and contrast it with standard Expectation Maximization (EM) algorithms used to estimate Kalman filter parameters. We show that our algorithm is a consistent estimation procedure and present preliminary empirical results suggesting that our algorithm outperforms EM, particularly as the model dimension increases.
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PDF链接：
https://arxiv.org/pdf/1207.1416