论文标题
快速使用简单可逆跳动移动的贝叶斯反卷积
Fast Bayesian Deconvolution using Simple Reversible Jump Moves
论文作者
论文摘要
我们提出了马尔可夫链基于蒙特卡洛的反卷积方法，旨在估计光谱数据中的峰数，以及每个径向基础函数的最佳参数。假设峰值数量未知，并且在所有候选模型上进行扫描模拟在计算上是不现实的，则提出的方法有效地通过跨维动作有效地搜索了可能的候选者，这些移动受复制品交换蒙特卡洛移动的退火效应的帮助。通过使用合成数据的模拟，该提出的方法证明了其优于常规的扫描模拟，尤其是在模型选择问题中。应用于一组橄榄石反射频谱数据，其叶状岩和Fayalite混合物比率不同，从以前的矿物学研究中获得了结果，这表明我们的方法适用于对真实数据集的反vlution。
We propose a Markov chain Monte Carlo-based deconvolution method designed to estimate the number of peaks in spectral data, along with the optimal parameters of each radial basis function. Assuming cases where the number of peaks is unknown, and a sweep simulation on all candidate models is computationally unrealistic, the proposed method efficiently searches over the probable candidates via trans-dimensional moves assisted by annealing effects from replica exchange Monte Carlo moves. Through simulation using synthetic data, the proposed method demonstrates its advantages over conventional sweep simulations, particularly in model selection problems. Application to a set of olivine reflectance spectral data with varying forsterite and fayalite mixture ratios reproduced results obtained from previous mineralogical research, indicating that our method is applicable to deconvolution on real data sets.
