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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 1475-6064

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Abstract

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: Article
Journal or Publication Title: Advances in Applied Probability
Uncontrolled Keywords: Coupling from the past ; MCMC ; perfect sampling ; rejection sampling ; trees and forests.
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 1231
Deposited By: Mr Hongsheng Dai
Deposited On: 05 Feb 2008 11:53
Refereed?: No
Published?: Published
Last Modified: 26 Jul 2012 15:03
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/1231

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