Article
Analysis of Convergence Rates of Some Gibbs Samplers on Continuous State Spaces
Stochastic Processes and their Applications (Impact Factor: 0.95). 08/2011; DOI: 10.1016/j.spa.2013.05.003
Source: arXiv
 Citations (15)
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Article: Hitandrun mixes fast
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ABSTRACT: It is shown that the “hitandrun” algorithm for sampling from a convex body K (introduced by R.L. Smith) mixes in time O *(n 2 R 2/r 2), where R and r are the radii of the inscribed and circumscribed balls of K. Thus after appropriate preprocessing, hitandrun produces an approximately uniformly distributed sample point in time O *(n 3), which matches the best known bound for other sampling algorithms. We show that the bound is best possible in terms of R,r and n.Mathematical Programming 11/1999; 86(3):443461. · 2.09 Impact Factor 
Conference Paper: Path coupling: A technique for proving rapid mixing in Markov chains
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ABSTRACT: The main technique used in algorithm design for approximating heart of the method is the study of the convergence (mixing) rates of particular Markov chains of interest. In this paper we illustrate a new approach to the coupling technique, which we call path coupling, for bounding mixing rates. Previous applications of coupling have required detailed insights into the combinatorics of the problem at hand, and this complexity can make the technique extremely difficult to apply successfully. Path coupling helps to minimize the combinatorial difficulty and in all cases provides simpler convergence proofs than does the standard coupling method. However the true power of the method is that the simplification obtained may allow coupling proofs which were previously unknown, or provide significantly better bounds than those obtained using the standard method. We apply the path coupling method to several hard combinatorial problems, obtaining new or improved results. We examine combinatorial problems such as graph colouring and TWICESAT, and problems from statistical physics, such as the antiferromagnetic Potts model and the hardcore lattice gas model. In each case we provide either a proof of rapid mixing where none was known previously, or substantial simplification of existing proofs with consequent gains in the performance of the resulting algorithmsFoundations of Computer Science, 1997. Proceedings., 38th Annual Symposium on; 11/1997 
Conference Paper: Mixing Points on a Circle.
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ABSTRACT: We determine, up to a log factor, the mixing time of a Markov chain whose state space consists of the successive distances between n labeled “dots” on a circle, in which one dot is selected uniformly at random and moved to a uniformly random point between its two neighbors. The method involves novel use of auxiliary discrete Markov chains to keep track of a vector of quadratic parameters.Approximation, Randomization and Combinatorial Optimization, Algorithms and Techniques, 8th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems, APPROX 2005 and 9th InternationalWorkshop on Randomization and Computation, RANDOM 2005, Berkeley, CA, USA, August 2224, 2005, Proceedings; 01/2005
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