Sequential Decision Model for Inference and Prediction on Non-Uniform Hypergraphs with Application to Knot Matching from Computational Forestry
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.
Keywords
Cite
@article{arxiv.1708.07592,
title = {Sequential Decision Model for Inference and Prediction on Non-Uniform Hypergraphs with Application to Knot Matching from Computational Forestry},
author = {Seong-Hwan Jun and Samuel W. K. Wong and James V. Zidek and Alexandre Bouchard-Côté},
journal= {arXiv preprint arXiv:1708.07592},
year = {2017}
}
Comments
32 pages, 14 figures, submitted to Annals of Applied Statistics