dorsal/arxiv
View SchemaMarkov Chains of Infinite Order and Asymptotic Satisfaction of Balance: Application to the Adaptive Integration Method
| Authors | David J. Earl, Michael W. Deem |
|---|---|
| Categories | |
| ArXiv ID | physics/0411150 |
| URL | https://arxiv.org/abs/physics/0411150 |
Abstract
Adaptive Monte Carlo methods can be viewed as implementations of Markov chains with infinite memory. We derive a general condition for the convergence of a Monte Carlo method whose history dependence is contained within the simulated density distribution. In convergent cases, our result implies that the balance condition need only be satisfied asymptotically. As an example, we show that the adaptive integration method converges.
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"abstract": "Adaptive Monte Carlo methods can be viewed as implementations of Markov\nchains with infinite memory. We derive a general condition for the convergence\nof a Monte Carlo method whose history dependence is contained within the\nsimulated density distribution. In convergent cases, our result implies that\nthe balance condition need only be satisfied asymptotically. As an example, we\nshow that the adaptive integration method converges.",
"arxiv_id": "physics/0411150",
"authors": [
"David J. Earl",
"Michael W. Deem"
],
"categories": [
"physics.comp-ph",
"cond-mat.stat-mech"
],
"title": "Markov Chains of Infinite Order and Asymptotic Satisfaction of Balance: Application to the Adaptive Integration Method",
"url": "https://arxiv.org/abs/physics/0411150"
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