Perfect sampling methods for random forests

Dai, Hongsheng (2008) Perfect sampling methods for random forests Advances in Applied Probability, 40 (3). pp. 897-917. ISSN 0001-8678

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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: 12 Mar 2015 11:05

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