Variational approximations for stochastic dynamics on graphs
| Autor: | Marco Pretti, Alessandro Pelizzola |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Random graph Statistical Mechanics (cond-mat.stat-mech) Monte Carlo method FOS: Physical sciences Analogy Statistical and Nonlinear Physics 01 natural sciences 010305 fluids & plasmas dynamical processes Stochastic dynamics ynamical processes networks stationary states 0103 physical sciences stochastic processes Statistical physics Statistics Probability and Uncertainty 010306 general physics random graphs Condensed Matter - Statistical Mechanics Mathematics |
| Zdroj: | BIFI International Conference 2018-Complexity, networks and collective behaviour, Zaragoza, Spain, 6-8/2/2018 info:cnr-pdr/source/autori:Marco Pretti (1), Alessandro Pelizzola (2)/congresso_nome:BIFI International Conference 2018-Complexity, networks and collective behaviour/congresso_luogo:Zaragoza, Spain/congresso_data:6-8%2F2%2F2018/anno:2018/pagina_da:/pagina_a:/intervallo_pagine Journal of statistical mechanics 2017 (2017). doi:10.1088/1742-5468/aa7a40 info:cnr-pdr/source/autori:Pelizzola A.; Pretti M./titolo:Variational approximations for stochastic dynamics on graphs/doi:10.1088%2F1742-5468%2Faa7a40/rivista:Journal of statistical mechanics/anno:2017/pagina_da:/pagina_a:/intervallo_pagine:/volume:2017 |
| DOI: | 10.1088/1742-5468/aa7a40 |
| Popis: | We investigate different mean-field-like approximations for stochastic dynamics on graphs, within the framework of a cluster-variational approach. In analogy with its equilibrium counterpart, this approach allows one to give a unified view of various (previously known) approximation schemes, and suggests quite a systematic way to improve the level of accuracy. We compare the different approximations with Monte Carlo simulations on a reversible (susceptible-infected-susceptible) discrete-time epidemic-spreading model on random graphs. 29 pages, 5 figures. Minor revisions. IOP-styled |
| Databáze: | OpenAIRE |
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