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Probability Density Estimation-Non-Parametric Methods(概率密度估计-非参数方法)

1. Kernel / k-Nearest Neighborhood Density Estimators (核密度估计 / K邻近密度估计)

• KDE: Fix volume, determine number of points in this volume
• K-NN: Fix the number of points and increase the volume to include this number of points

python apply.py

2. Expectation Maximization (EM) Algo for Gaussian Mixture Model (GMM) （应用于高斯混合模型的期望最大化算法）

• EM algo is sensible to init, we can use k-means fist for some steps kmeans = KMeans(n_clusters = K, n_init = 10).fit(data)

python apply.py