英文标题：
《Noise Robust Online Inference for Linear Dynamic Systems》
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作者：
Saikat Saha
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最新提交年份：
2015
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英文摘要：
  We revisit the Bayesian online inference problems for the linear dynamic systems (LDS) under non- Gaussian environment. The noises can naturally be non-Gaussian (skewed and/or heavy tailed) or to accommodate spurious observations, noises can be modeled as heavy tailed. However, at the cost of such noise robustness, the performance may degrade when such spurious observations are absent. Therefore, any inference engine should not only be robust to noise outlier, but also be adaptive to potentially unknown and time varying noise parameters; yet it should be scalable and easy to implement.   To address them, we envisage here a new noise adaptive Rao-Blackwellized particle filter (RBPF), by leveraging a hierarchically Gaussian model as a proxy for any non-Gaussian (process or measurement) noise density. This leads to a conditionally linear Gaussian model (CLGM), that is tractable. However, this framework requires a valid transition kernel for the intractable state, targeted by the particle filter (PF). This is typically unknown. We outline how such kernel can be constructed provably, at least for certain classes encompassing many commonly occurring non-Gaussian noises, using auxiliary latent variable approach. The efficacy of this RBPF algorithm is demonstrated through numerical studies.
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中文摘要：
我们重新研究了非高斯环境下线性动态系统（LDS）的贝叶斯在线推理问题。噪声自然可以是非高斯（倾斜和/或重尾）的，或者为了适应虚假观测，可以将噪声建模为重尾。然而，以这种噪声鲁棒性为代价，当没有这种虚假观测时，性能可能会下降。因此，任何推理机不仅要对噪声异常值具有鲁棒性，还要对潜在的未知和时变噪声参数具有自适应性；然而，它应该是可扩展的，并且易于实现。为了解决这些问题，我们在这里设想了一种新的噪声自适应Rao Blackwellized粒子滤波器（RBPF），它利用分层高斯模型作为任何非高斯（过程或测量）噪声密度的代理。这导致了一个条件线性高斯模型（CLGM），这是易于处理的。然而，该框架需要一个有效的过渡内核，用于粒子滤波器（PF）针对的棘手状态。这通常是未知的。我们概述了如何使用辅助潜变量方法，至少对于包含许多常见非高斯噪声的特定类，可证明地构造这样的核。数值研究证明了RBPF算法的有效性。
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分类信息：

一级分类：Statistics        统计学
二级分类：Computation        计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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一级分类：Computer Science        计算机科学
二级分类：Systems and Control        系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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