Corpus ID: 433545

The Markov chain Monte Carlo method: an approach to approximate counting and integration

@inproceedings{Jerrum1996TheMC,
  title={The Markov chain Monte Carlo method: an approach to approximate counting and integration},
  author={M. Jerrum and A. Sinclair},
  year={1996}
}
In the area of statistical physics, Monte Carlo algorithms based on Markov chain simulation have been in use for many years. The validity of these algorithms depends crucially on the rate of convergence to equilibrium of the Markov chain being simulated. Unfortunately, the classical theory of stochastic processes hardly touches on the sort of non-asymptotic analysis required in this application. As a consequence, it had previously not been possible to make useful, mathematically rigorous… Expand
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References

SHOWING 1-10 OF 132 REFERENCES
Exact sampling with coupled Markov chains and applications to statistical mechanics
Improved Bounds for Mixing Rates of Marcov Chains and Multicommodity Flow
  • A. Sinclair
  • Mathematics, Computer Science
  • Comb. Probab. Comput.
  • 1992
New Monte Carlo method for the self-avoiding walk
Approximating the Permanent
...
1
2
3
4
5
...