动态广义线性模型的在线状态和参数估计
Online state and parameter estimation in Dynamic Generalised Linear Models
摘要：
流时间序列的推理与贝叶斯在线状态和参数推理问题紧密耦合。在本文中，我们将介绍动态广义线性模型，一类模型经常选择连续和离散时间序列数据建模。我们将研究三种不同的方法，这些方法允许在线估计并分析应用于与流数据推断相关的不同现实世界数据集时的结果。基于充分统计量的方法延迟了已知问题，例如粒子的离散化，特别是当应用于长时间运行的时间序列时，同时与精确方法（例如粒子边缘Metropolis-Hastings）相比，提供合理的参数估计。状态和观测预报也将作为业绩指标进行分析。通过对“金标准”（离线）方法进行基准测试，我们可以更好地了解在线方法在具有挑战性的现实世界场景中的性能。
英文摘要：
Inference for streaming time-series is tightly coupled with the problem of Bayesian on-line state and parameter inference. In this paper we will introduce Dynamic Generalised Linear Models, the class of models often chosen to model continuous and discrete time-series data. We will look at three different approaches which allow on-line estimation and analyse the results when applied to different real world datasets related to inference for streaming data. Sufficient statistics based methods delay known problems, such as particle impoverishment, especially when applied to long running time-series, while providing reasonable parameter estimations when compared to exact methods, such as Particle Marginal Metropolis-Hastings. State and observation forecasts will also be analysed as a performance metric. By benchmarking against a "gold standard" (off-line) method, we can better understand the performance of on-line methods in challenging real-world scenarios.
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