变点模型的演化序贯蒙特卡罗采样器

A. Dufays
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引用次数: 11

摘要

序贯蒙特卡罗(SMC)方法广泛用于非线性滤波。然而,SMC范围包括更广泛的应用,例如估计静态模型参数,因此它正在成为马尔可夫链蒙特卡罗(MCMC)方法的重要替代方案。SMC算法不仅绘制静态或动态参数的后验分布,而且还提供了边际似然的估计。本文开发的回火时间(TNT)算法将(离线)回火SMC推理与在线SMC推理相结合,在不遇到粒子退化问题的情况下从许多顺序后验分布中绘制实现。此外,它还引入了一个新的MCMC恢复步骤,该步骤是通用的、自动化的,非常适合多模态分布。由于此更新依赖于广泛的启发式优化文献,因此已经有许多扩展可用。该算法特别适合于估计变化点模型。作为一个例子,我们通过它们随时间的边际可能性来比较变点GARCH模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evolutionary Sequential Monte Carlo Samplers for Change-Point Models
Sequential Monte Carlo (SMC) methods are widely used for non-linear filtering purposes. Nevertheless the SMC scope encompasses wider applications such as estimating static model parameters so much that it is becoming a serious alternative to Markov-Chain Monte-Carlo (MCMC) methods. Not only SMC algorithms draw posterior distributions of static or dynamic parameters but additionally provide an estimate of the marginal likelihood. The tempered and time (TNT) algorithm, developed in the paper, combines (off-line) tempered SMC inference with on-line SMC inference for drawing realizations from many sequential posterior distributions without experiencing a particle degeneracy problem. Furthermore, it introduces a new MCMC rejuvenation step that is generic, automated and well-suited for multi-modal distributions. As this update relies on the wide heuristic optimization literature, numerous extensions are already available. The algorithm is notably appropriate for estimating Change-point models. As an example, we compare Change-point GARCH models through their marginal likelihoods over time.
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