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Perfect sampling methods for random forests

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

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Abstract

A weighted graph G is a pair (V, ε) containing vertex set V and edge set ε, where each edge e ∈ ε 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 (CFrP) and rejection sampling are presented. Comparisons of these methods are given theoretically and via simulation. © Applied Probability Trust 2008.

Item Type: Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 12 Feb 2013 08:07
Last Modified: 04 Feb 2019 16:15
URI: http://repository.essex.ac.uk/id/eprint/5493

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