Reflection, Refraction, and Hamiltonian Monte Carlo

被引:0
作者
Afshar, Hadi Mohasel [1 ]
Domke, Justin [2 ,3 ]
机构
[1] Australian Natl Univ, Res Sch Comp Sci, Canberra, ACT 0200, Australia
[2] NICTA, Canberra, ACT 0200, Australia
[3] Australian Natl Univ, Canberra, ACT 0200, Australia
来源
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 28 (NIPS 2015) | 2015年 / 28卷
基金
澳大利亚研究理事会;
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Hamiltonian Monte Carlo (HMC) is a successful approach for sampling from continuous densities. However, it has difficulty simulating Hamiltonian dynamics with non-smooth functions, leading to poor performance. This paper is motivated by the behavior of Hamiltonian dynamics in physical systems like optics. We introduce a modification of the Leapfrog discretization of Hamiltonian dynamics on piecewise continuous energies, where intersections of the trajectory with discontinuities are detected, and the momentum is reflected or refracted to compensate for the change in energy. We prove that this method preserves the correct stationary distribution when boundaries are affine. Experiments show that by reducing the number of rejected samples, this method improves on traditional HMC.
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页数:9
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