CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

A Method For Inferring Hierarchical Dynamics In Stochastic Processes

Martin Nilsson Jacobi (Institutionen för energi och miljö, Fysisk resursteori) ; Olof Görnerup (Institutionen för energi och miljö, Fysisk resursteori)
Advances in Complex Systems (0219-5259). Vol. 11 (2008), 1, p. 1-16.
[Artikel, refereegranskad vetenskaplig]

Complex systems may often be characterized by their hierarchical dynamics. In this paper we present a method and an operational algorithm that automatically infer this property in a broad range of systems discrete stochastic processes. The main idea is to systematically explore the set of projections from the state space of a process to smaller state spaces, and to determine which of the projections impose Markovian dynamics on the coarser level. These projections, which we call Markov projections, then constitute the hierarchical dynamics of the system. The algorithm operates on time series or other statistics, so a priori knowledge of the intrinsic workings of a system is not required in order to determine its hierarchical dynamics. We illustrate the method by applying it to two simple processes a finite state automaton and an iterated map.

Nyckelord: Coarse-graining; Hierarchical dynamics; Model reduction



Denna post skapades 2009-01-30. Senast ändrad 2016-08-18.
CPL Pubid: 89469

 

Institutioner (Chalmers)

Institutionen för energi och miljö, Fysisk resursteori

Ämnesområden

Fysik

Chalmers infrastruktur