Perfect sampling methods for random forests

Perfect sampling methods for random forests

Hongsheng Dai

Source: Adv. in Appl. Probab. Volume 40, Number 3 (2008), 897-917.

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 W_e. 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.

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Primary Subjects: 65C05, 65C50

Secondary Subjects: 05C80

Keywords: Coupling from the past; MCMC; perfect sampling; rejection sampling; trees and forests

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Links and Identifiers

Permanent link to this document: http://projecteuclid.org/euclid.aap/1222868191
Digital Object Identifier: doi:10.1239/aap/1222868191
Mathematical Reviews number (MathSciNet): MR2454038
Zentralblatt MATH identifier: 1160.05334

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