Unbiased Smoothing using Particle Independent Metropolis-Hastings
Unbiased Smoothing using Particle Independent Metropolis-Hastings
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DOI：
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发表时间：
2019-02
影响因子：
2.8
通讯作者：
Lawrence Middleton;George Deligiannidis;A. Doucet;P. Jacob
中科院分区：
文献类型：
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作者：
Lawrence Middleton;George Deligiannidis;A. Doucet;P. Jacob
我们考虑的期望值的近似分布的一个潜在的马尔可夫过程噪声测量。这被称为平滑问题，通常使用粒子和马尔可夫链蒙特卡罗（MCMC）方法来处理。这些方法在有限时间内运行时提供一致但有偏的估计。我们提出了一种简单的方法耦合两个MCMC链使用粒子独立的Metropolis-Hastings（PIMH）生成无偏平滑估计。无偏估计量在并行计算的背景下是有吸引力的，并且便于置信区间的构造。该方案只需要访问现成的粒子滤波器（PF），因此比最近提出的无偏平滑更容易实现。该方法被证明是一个利维驱动的随机波动率模型和随机动力学模型。
We consider the approximation of expectations with respect to the distribution of a latent Markov process given noisy measurements. This is known as the smoothing problem and is often approached with particle and Markov chain Monte Carlo (MCMC) methods. These methods provide consistent but biased estimators when run for a finite time. We propose a simple way of coupling two MCMC chains built using Particle Independent Metropolis–Hastings (PIMH) to produce unbiased smoothing estimators. Unbiased estimators are appealing in the context of parallel computing, and facilitate the construction of confidence intervals. The proposed scheme only requires access to off-the-shelf Particle Filters (PF) and is thus easier to implement than recently proposed unbiased smoothers. The approach is demonstrated on a Levy-driven stochastic volatility model and a stochastic kinetic model.