作者: A. B. Dieker , Santosh S. Vempala
关键词: Discrete mathematics 、 Markov chain Monte Carlo 、 Markov chain 、 Coupling from the past 、 Mathematics 、 Boundary (topology) 、 Convex set 、 Applied mathematics 、 Slice sampling 、 Markov chain mixing time 、 Markov property
摘要: Stochastic billiards can be used for approximate sampling from the boundary of a bounded convex set through Markov Chain Monte Carlo paradigm. This paper studies how many steps underlying chain are required to get samples (approximately) uniform distribution on set, sets with an upper bound curvature boundary. Our main theorem implies polynomial-time algorithm such sets.