Efficient algorithm for the forest fire model

Physical Review E - Statistical, Nonlinear, and Soft Matter Physics

Volumn 70, Issue 6 2, 2004, Pages

  Pruessner, Gunnar ^a   Jensen, Henrik Jeldtoft ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; ENCODING (SYMBOLS); LATTICE CONSTANTS; PROBABILITY; PROBLEM SOLVING; SPATIAL VARIABLES CONTROL; STATE SPACE METHODS; STATISTICAL METHODS;

CLUSTERING; DIVERGENT MOMENTS; DROSSEL-SCHWABL FOREST FIRE MODEL (DS-FFM); SCALING;

PARALLEL ALGORITHMS;

EID: 37649028747     PISSN: 15393755     EISSN: 15502376     Source Type: Journal    
DOI: 10.1103/PhysRevE.70.066707     Document Type: Article

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53. M) representing the conditional average.
54. Similarly for other forms of path compression, for example bending the pointer of the preceding to the adjacent site in find_root (Fig. 8).
55. For integers the precision is not a problem, but the maximum representable number easily becomes a problem.
56. One might be inclined to postpone the sending of the borders to a time when it is really needed. However, after a successful burning, the time t is increased and this enters the histogram (see Sec. II C 3). Ignoring this change for a large number of steps would introduce uncontrollable deviations of the estimator of the histogram from its true value.