确保异步吉布斯采样的快速混合和低偏差
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
摘要：
吉布斯抽样是一种马尔可夫链蒙特卡罗技术，通常用于估计边际分布。为了加快吉布斯采样，最近有兴趣通过异步执行并行化。虽然实证结果表明，许多模型可以有效地异步采样，传统的马尔可夫链分析不适用于异步的情况下，因此异步吉布斯采样知之甚少。在本文中，我们得到一个更好的理解异步吉布斯的两个主要挑战：偏见和混合时间。我们的实验表明，我们的理论结果与实际结果相匹配。
英文摘要：
Gibbs sampling is a Markov chain Monte Carlo technique commonly used for estimating marginal distributions. To speed up Gibbs sampling, there has recently been interest in parallelizing it by executing asynchronously. While empirical results suggest that many models can be efficiently sampled asynchronously, traditional Markov chain analysis does not apply to the asynchronous case, and thus asynchronous Gibbs sampling is poorly understood. In this paper, we derive a better understanding of the two main challenges of asynchronous Gibbs: bias and mixing time. We show experimentally that our theoretical results match practical outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
