基于序贯蒙特卡罗抽样的广义线性混合模型分析
Generalised linear mixed model analysis via sequential Monte Carlo sampling
摘要：
提出了一种用于广义线性混合模型贝叶斯分析的序贯蒙特卡罗抽样算法。这些模型支持各种有趣的回归类型的分析，但执行推理往往是非常困难的，即使使用贝叶斯方法结合马尔可夫链蒙特卡罗（MCMC）。序贯蒙特卡罗抽样法是一种新的、通用的从后验分布中产生样本的方法。在这篇文章中，我们演示了使用SMC方法进行GLP-I推理。我们证明了模拟和真实数据的方法的有效性，并发现，序贯蒙特卡罗是一个有竞争力的替代MCMC技术。
英文摘要：
We present a sequential Monte Carlo sampler algorithm for the Bayesian analysis of generalised linear mixed models (GLMMs). These models support a variety of interesting regression-type analyses, but performing inference is often extremely difficult, even when using the Bayesian approach combined with Markov chain Monte Carlo (MCMC). The Sequential Monte Carlo sampler (SMC) is a new and general method for producing samples from posterior distributions. In this article we demonstrate use of the SMC method for performing inference for GLMMs. We demonstrate the effectiveness of the method on both simulated and real data, and find that sequential Monte Carlo is a competitive alternative to the available MCMC techniques.
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