Polynomial-time approximation algorithms for the Ising model
SIAM Journal on Computing
Randomized algorithms
Exact sampling with coupled Markov chains and applications to statistical mechanics
Proceedings of the seventh international conference on Random structures and algorithms
Exact sampling and approximate counting techniques
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Efficient exact sampling from the Ising model using Swendsen-Wang
Proceedings of the tenth annual ACM-SIAM symposium on Discrete algorithms
A faster method for sampling independent sets
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Mixing properties of the Swendsen-Wang process on classes of graphs
Random Structures & Algorithms - Special issue on statistical physics methods in discrete probability, combinatorics, and theoretical computer science
Perfect sampling using bounding chains
Perfect sampling using bounding chains
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.00 |
The greatest drawback of Monte Carlo Markov chain methods is lack of knowledge of the mixing time of the chain. The use of bounding chains solves this difficulty for some chains by giving theoretical and experimental upper bounds on the mixing time. Moreover, when used with methodologies such as coupling from the past, bounding chains allow the user to take samples drawn exactly from the stationary distribution without knowledge of the mixing time. Here we present a bounding chain for the Swendsen-Wang process. The Swendsen-Wang bounding chain allow us to efficiently obtain exact samples from the ferromagnetic Q-state Potts model for certain classes of graphs. Also, by analyzing this bounding chain, we will show that Swendsen-Wang is rapidly mixing over a slightly larger range of parameters than was known previously.