最大的高斯混合模型似然估计的期望最大化算法-Maximum likelihood estimation of Gaussian mixture model by expectation maximization algorithm

Traditional single particle reconstruction methods use either the Fourier or the delta function basis to represent the particle density map. We propose a more flexible algorithm that adaptively chooses the basis based on the data. Because the b

Expectation Maximization algorithm for Gaussian Mixture Model Training