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September 2019 Sequential decision model for inference and prediction on nonuniform hypergraphs with application to knot matching from computational forestry
Seong-Hwan Jun, Samuel W. K. Wong, James V. Zidek, Alexandre Bouchard-Côté
Ann. Appl. Stat. 13(3): 1678-1707 (September 2019). DOI: 10.1214/19-AOAS1255

Abstract

In this paper, we consider the knot-matching problem arising in computational forestry. The knot-matching problem is an important problem that needs to be solved to advance the state of the art in automatic strength prediction of lumber. We show that this problem can be formulated as a quadripartite matching problem and develop a sequential decision model that admits efficient parameter estimation along with a sequential Monte Carlo sampler on graph matching that can be utilized for rapid sampling of graph matching. We demonstrate the effectiveness of our methods on 30 manually annotated boards and present findings from various simulation studies to provide further evidence supporting the efficacy of our methods.

Citation

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Seong-Hwan Jun. Samuel W. K. Wong. James V. Zidek. Alexandre Bouchard-Côté. "Sequential decision model for inference and prediction on nonuniform hypergraphs with application to knot matching from computational forestry." Ann. Appl. Stat. 13 (3) 1678 - 1707, September 2019. https://doi.org/10.1214/19-AOAS1255

Information

Received: 1 September 2018; Revised: 1 February 2019; Published: September 2019
First available in Project Euclid: 17 October 2019

zbMATH: 07145972
MathSciNet: MR4019154
Digital Object Identifier: 10.1214/19-AOAS1255

Keywords: computational forestry , expectation maximization , Graph matching , Plackett–Luce model , sequential Monte Carlo

Rights: Copyright © 2019 Institute of Mathematical Statistics

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Vol.13 • No. 3 • September 2019
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