基于贝叶斯证据框架下的最小二乘小波支持向量机，设计了一种新型模拟电路故障诊断方法。
Based on least squares wavelet support vector machines (LS-WSVM) within the Bayesian evidence framework, a systematic method for fault diagnosis of analog circuits was proposed.
通过蒙特卡罗模拟和实例表明多层贝叶斯估计比最大似然估计更加有效。
It is seen that hierarchical Bayesian estimation is more efficient than maximum likelihood estimation through Monte Carlo simulation and an example.
我介绍了一个系统的方法应用贝叶斯统计推论和最大熵来处理量子蒙特卡罗模拟所得到连续虚时间的数据。
We present a systematical way to use Bayesian statistical inference and the maximum entropy to deal with the data obtained by the continue imaginary-time QMC simulations.
应用推荐