论文标题
运输椭圆形采样
Transport Elliptical Slice Sampling
论文作者
论文摘要
我们提出了一个新的框架，以使用标准化流量和椭圆形切成采样的组合有效地从复杂概率分布中进行采样（Murray等，2010）。核心思想是通过标准化流量来学习差异性，该流量将目标分布的非高斯结构映射到大约高斯分布。然后，我们使用椭圆形采样器，这是一种有效且无调的马尔可夫链蒙特卡洛（MCMC）算法，从转化的分布中采样。然后，使用逆归一流的流量将样品向后拉，从而产生近似固定目标分布的样品。我们的运输椭圆形采样器（TESS）针对现代计算机架构进行了优化，在现代计算机架构中，其适应机制利用并行核心快速运行多个马尔可夫链进行一些迭代。数值证明表明，与非转化的采样器相比，苔丝从目标分布中产生蒙特卡洛样品，与非转化的采样器相比，与非转化的采样器相比，与针对平行计算机体系结构设计的基于梯度的建议相比，效率的显着提高，给定足够灵活的差异。
We propose a new framework for efficiently sampling from complex probability distributions using a combination of normalizing flows and elliptical slice sampling (Murray et al., 2010). The central idea is to learn a diffeomorphism, through normalizing flows, that maps the non-Gaussian structure of the target distribution to an approximately Gaussian distribution. We then use the elliptical slice sampler, an efficient and tuning-free Markov chain Monte Carlo (MCMC) algorithm, to sample from the transformed distribution. The samples are then pulled back using the inverse normalizing flow, yielding samples that approximate the stationary target distribution of interest. Our transport elliptical slice sampler (TESS) is optimized for modern computer architectures, where its adaptation mechanism utilizes parallel cores to rapidly run multiple Markov chains for a few iterations. Numerical demonstrations show that TESS produces Monte Carlo samples from the target distribution with lower autocorrelation compared to non-transformed samplers, and demonstrates significant improvements in efficiency when compared to gradient-based proposals designed for parallel computer architectures, given a flexible enough diffeomorphism.
