Dai, H. (2008) Perfect sampling methods for random forests Advances in Applied Probability, 40 (3). pp. 897-917. ISSN 0001-8678Full text not available from this repository.
A weighted graph G is a pair (V, E) containing vertex set V and edge set E, where each edge e ∈ E is associated with a weight We. A subgraph of G is a forest if it has no cycles. All forests on the graph G form a probability space, where the probability of each forest is proportional to the product of the weights of its edges. This paper aims to simulate forests exactly from the target distribution. Methods based on coupling from the past (CFTP) and rejection sampling are presented. Comparisons of these methods are given theoretically and via simulation.
|Item Type:||Journal article|
|Uncontrolled Keywords:||Coupling from the past; MCMC; perfect sampling; rejection sampling; trees and forests|
|Subjects:||G000 Computing and Mathematical Sciences|
|DOI (a stable link to the resource):||10.1239/aap/1222868191|
|Faculties:||Faculty of Science and Engineering > School of Computing, Engineering and Mathematics > Computational Mathematics|
|Depositing User:||editor cmis|
|Date Deposited:||14 Jan 2011 10:35|
|Last Modified:||03 May 2012 09:18|
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