非一致超图推理预测的序贯决策模型及其在计算林业结点匹配中的应用
Sequential Decision Model for Inference and Prediction on Non-Uniform Hypergraphs with Application to Knot Matching from Computational Forestry
摘要：
在本文中，我们考虑了计算林业中出现的节点匹配问题。节点匹配问题是提高木材强度自动预测技术水平所必须解决的重要问题。我们表明，这个问题可以制定为一个四分匹配问题，并开发一个顺序决策模型，承认有效的参数估计与序贯蒙特卡罗采样器的图形匹配，可用于快速采样的图形匹配。我们证明了我们的方法的有效性30手动注释板和目前的研究结果，从各种模拟研究提供进一步的证据支持我们的方法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
