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Perfect Sampling Methods For Random Forests.

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Perfect Sampling Methods For Random Forests. / Dai, Hongsheng.
In: Advances in Applied Probability, Vol. 40, No. 3, 2008, p. 897-917.

Research output: Contribution to Journal/Magazine › Journal article

Harvard

Dai, H 2008, 'Perfect Sampling Methods For Random Forests.', Advances in Applied Probability, vol. 40, no. 3, pp. 897-917. https://doi.org/10.1239/aap/1222868191

APA

Dai, H. (2008). Perfect Sampling Methods For Random Forests. Advances in Applied Probability, 40(3), 897-917. https://doi.org/10.1239/aap/1222868191

Vancouver

Dai H. Perfect Sampling Methods For Random Forests. Advances in Applied Probability. 2008;40(3):897-917. doi: 10.1239/aap/1222868191

Author

Dai, Hongsheng. / Perfect Sampling Methods For Random Forests. In: Advances in Applied Probability. 2008 ; Vol. 40, No. 3. pp. 897-917.

Bibtex

@article{6f20664664f84bf99130b5419c2fbfb7,
title = "Perfect Sampling Methods For Random Forests.",
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.",
keywords = "Coupling from the past, MCMC, perfect sampling, rejection sampling, trees and forests.",
author = "Hongsheng Dai",
year = "2008",
doi = "10.1239/aap/1222868191",
language = "English",
volume = "40",
pages = "897--917",
journal = "Advances in Applied Probability",
issn = "1475-6064",
publisher = "Cambridge University Press",
number = "3",

}

RIS

TY - JOUR

T1 - Perfect Sampling Methods For Random Forests.

AU - Dai, Hongsheng

PY - 2008

Y1 - 2008

N2 - 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.

AB - 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.

KW - Coupling from the past

KW - MCMC

KW - perfect sampling

KW - rejection sampling

KW - trees and forests.

U2 - 10.1239/aap/1222868191

DO - 10.1239/aap/1222868191

M3 - Journal article

VL - 40

SP - 897

EP - 917

JO - Advances in Applied Probability

JF - Advances in Applied Probability

SN - 1475-6064

IS - 3

ER -