Title :
Rapidly mixing Markov chains with applications in computer science and physics
Author_Institution :
Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA
Abstract :
Monte Carlo algorithms often depend on Markov chains to sample from very large data sets. A key ingredient in the design of an efficient Markov chain is determining rigorous bounds on how quickly the chain "mixes," or converges, to its stationary distribution. This survey provides an overview of several useful techniques
Keywords :
Markov processes; computer science; physics; Markov chains; computer science; physics applications; Algorithm design and analysis; Application software; Computer science; Lattices; Monte Carlo methods; Nearest neighbor searches; Pervasive computing; Physics computing; Sampling methods; State-space methods; Markov processes; Monte Carlo simulation; analysis of algorithm and problem complexity;
Journal_Title :
Computing in Science & Engineering
DOI :
10.1109/MCSE.2006.30
