SEQUENTIAL DECISION MODEL FOR INFERENCE AND PREDICTION ON NONUNIFORM HYPERGRAPHS WITH APPLICATION TO KNOT MATCHING FROM COMPUTATIONAL FORESTRY

• 成果类型: Article
• 署名作者: Jun, Seong-Hwan; Wong, Samuel W. K.; Zidek, James, V; Bouchard-Cote, Alexandre
• 署名单位: University of British Columbia; University of Waterloo
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/19-AOAS1255
• 发表日期: 2019
• 页码: 1678-1707
• 关键词: phylogenetic inference; bending strength; wood; SYSTEM; TIMBER

摘要

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.