TOPICS
Search

Glauber Dynamics


Glauber dynamics is a Markov process that updates one spin at a time and preserves a specified Gibbs measure. In the heat-bath version, each site's rate-one clock replaces its spin by a sample from the conditional Gibbs measure given all other spins. For n sites, its generator is Lf=sum_(i=1)^(n)(P_if-f), where P_i is conditional averaging at site i. The discrete-time version chooses a uniform site at each attempted update. Its transition matrix is P=n^(-1)sum_(i)P_i, so L=n(P-I). Time measured in attempted updates is therefore n times the rate-one-per-site time scale.


See also

Autocorrelation, Gibbs Measure, Markov Chain, Markov Chain Cutoff, Mixing Time, Spectral Gap

Explore with Wolfram|Alpha

References

Levin, D. A. and Peres, Y. Markov Chains and Mixing Times, 2nd ed. With contributions by E. L. Wilmer. Providence, RI: American Mathematical Society, 2017. https://doi.org/10.1090/mbk/107.

Cite this as:

Weisstein, Eric W. "Glauber Dynamics." From MathWorld--A Wolfram Resource. https://mathworld.wolfram.com/GlauberDynamics.html

Subject classifications