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Rapidly Mixing Markov Chains with Applications in Computer Science and Physics
March/April 2006 (vol. 8 no. 2)
pp. 30-41
Dana Randall, Georgia Institute of Technology
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.
Index Terms:
Markov processes, Monte Carlo simulation, analysis of algorithm and problem complexity
Citation:
Dana Randall, "Rapidly Mixing Markov Chains with Applications in Computer Science and Physics," Computing in Science and Engineering, vol. 8, no. 2, pp. 30-41, March-April 2006, doi:10.1109/MCSE.2006.30
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