论文标题
Gibbs Zig-Zag采样器的后验计算
Posterior computation with the Gibbs zig-zag sampler
论文作者
论文摘要
最近提出了一种有趣的新一类分段确定性马尔可夫工艺（PDMP）作为马尔可夫链蒙特卡洛（MCMC）的替代方案。为了促进对更大类问题的应用程序，我们提出了一类称为gibbs Zig-Zag采样器的新类PDMPS，允许将参数应用于将某些参数应用于某些参数和传统MCMC风格更新的块中更新的块。我们证明了该框架对具有高维回归和随机效应的逻辑模型的后验采样的灵活性，并为几何层状性和中心极限定理的有效性提供了条件。
An intriguing new class of piecewise deterministic Markov processes (PDMPs) has recently been proposed as an alternative to Markov chain Monte Carlo (MCMC). In order to facilitate the application to a larger class of problems, we propose a new class of PDMPs termed Gibbs zig-zag samplers, which allow parameters to be updated in blocks with a zig-zag sampler applied to certain parameters and traditional MCMC-style updates to others. We demonstrate the flexibility of this framework on posterior sampling for logistic models with shrinkage priors for high-dimensional regression and random effects and provide conditions for geometric ergodicity and the validity of a central limit theorem.
