论文标题
锯齿形抽样，用于离散结构和非可逆的系统发育MCMC
Zig-zag sampling for discrete structures and non-reversible phylogenetic MCMC
论文作者
论文摘要
我们构建了针对由离散变量和连续变量组成的混合状态空间上定义的后验分布的ZIG-ZAG过程。该结构不需要对离散变量之间结构的任何假设。我们在基于Kingman合并的两个遗传学示例中演示了我们的方法，这表明锯齿形过程可以导致在经典大都市杂物算法上的效率提高多达几个数量级，并且非常适合平行计算。我们的施工类似于在混合状态空间上对汉密尔顿蒙特卡洛的现有技术，当应用于合并时，该技术在实施和分析复杂的边界交叉处遭受。我们证明，连续的锯齿形过程避免了这些并发症。
We construct a zig-zag process targeting a posterior distribution defined on a hybrid state space consisting of both discrete and continuous variables. The construction does not require any assumptions on the structure among discrete variables. We demonstrate our method on two examples in genetics based on the Kingman coalescent, showing that the zig-zag process can lead to efficiency gains of up to several orders of magnitude over classical Metropolis-Hastings algorithms, and that it is well suited to parallel computation. Our construction resembles existing techniques for Hamiltonian Monte Carlo on a hybrid state space, which suffers from implementationally and analytically complex boundary crossings when applied to the coalescent. We demonstrate that the continuous-time zig-zag process avoids these complications.
