We present 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 recurrent (susceptible-infected-susceptible) discrete-time epidemic-spreading model on random graphs.
Variational approximations for stochastic dynamics on graphs
Marco Pretti;
2018
Abstract
We present 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 recurrent (susceptible-infected-susceptible) discrete-time epidemic-spreading model on random graphs.File in questo prodotto:
| File | Dimensione | Formato | |
|---|---|---|---|
|
prod_423654-doc_150857.pdf
non disponibili
Descrizione: Variational approximations for stochastic dynamics on graphs
Tipologia:
Altro materiale allegato
Licenza:
Nessuna licenza dichiarata (non attribuibile a prodotti successivi al 2023)
Dimensione
413.28 kB
Formato
Adobe PDF
|
413.28 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
