• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Bayesian Inference and Maximum Entropy Methods in Science and Engineering >Physical Mixture Modeling with Unknown Number of Components
【24h】

Physical Mixture Modeling with Unknown Number of Components

机译：组件数量未知的物理混合物建模

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Measured physical spectra often comprise an unknown number of components of known parametric family. A reversible jump Markov chain Monte Carlo (RJMCMC) technique is applied to the problem of estimating the number of components evident in the data jointly with the parameters of the components. The physical model consists of a mixture of components, an additive background, and a convolution with a blurring apparatus transfer function. The results were compared with the deconvolution of a form-free distribution. By calculating marginal posterior probability density distributions from the RJMCMC sample for the most probable number of components we estimated the parameters and their uncertainties. The method was applied to a benchmark test of Rutherford backscattering spectroscopy on a system consisting of a thin Cu film where we know that Cu consists of two isotopes.
机译：测量的物理光谱通常包含未知数量的已知参数族的组件。可逆跳马尔可夫链蒙特卡洛（RJMCMC）技术应用于估计与数据中的参数一起存在于数据中的明显数量的问题。物理模型由混合的成分，附加的背景以及具有模糊设备传递函数的卷积组成。将结果与无形式分布的反卷积进行了比较。通过从RJMCMC样本中为最可能的组件数计算边际后验概率密度分布，我们估计了参数及其不确定性。该方法被用于卢瑟福背散射光谱的基准测试，该系统在由薄铜膜组成的系统上进行，其中我们知道铜由两个同位素组成。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号