论文标题
潜在切片采样算法
A Latent Slice Sampling Algorithm
论文作者
论文摘要
在本文中，我们引入了一种新的抽样算法，该算法有可能被用作大都市算法的普遍替代。它与切片采样器相关，并由一种算法进行，该算法适用于离散的概率分布％，可以将其视为在这种情况下的大都市 - 悬挂算法的替代方案，这消除了对提案分布的需求，在此中，这是没有接受/拒绝的组件。本文着眼于连续的对应物。潜在变量与切片采样器和应用于均匀密度函数的收缩过程相结合会产生一个高效的采样器，该采样器可以从非常高维分布作为单个块中生成随机变量。
In this paper we introduce a new sampling algorithm which has the potential to be adopted as a universal replacement to the Metropolis--Hastings algorithm. It is related to the slice sampler, and motivated by an algorithm which is applicable to discrete probability distributions %which can be viewed as an alternative to the Metropolis--Hastings algorithm in this setting, which obviates the need for a proposal distribution, in that is has no accept/reject component. This paper looks at the continuous counterpart. A latent variable combined with a slice sampler and a shrinkage procedure applied to uniform density functions creates a highly efficient sampler which can generate random variables from very high dimensional distributions as a single block.
