Transport Elliptical Slice Sampling
Transport Elliptical Slice Sampling
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DOI：
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发表时间：
2022-10
影响因子：
4.3
通讯作者：
A. Cabezas;C. Nemeth
中科院分区：
文献类型：
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作者：
A. Cabezas;C. Nemeth
我们提出了一个新的框架，利用归一化流和椭圆切片采样的组合，从复杂的概率分布中有效地采样（Murray等人，2010）。中心思想是学习一种微分同态，通过规范化流，将目标分布的非高斯结构映射到近似高斯分布。然后，我们使用椭圆切片采样器，一种高效且无需调谐的马尔可夫链蒙特卡罗（MCMC）算法，从变换后的分布中采样。然后使用反向归一化流将样本拉回，产生接近感兴趣的平稳目标分布的样本。我们的传输椭圆切片采样器（TESS）针对现代计算机体系结构进行了优化，其自适应机制利用并行核快速运行多个马尔可夫链进行几次迭代。数值演示表明，与未变换的采样器相比，TESS从目标分布产生的蒙特卡罗样本具有较低的自相关性，并且在给定足够灵活的微分同态的情况下，与设计用于并行计算机体系结构的基于梯度的方案相比，TESS的效率有了显著提高。
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.