请给出至少10种方法。这些方法需要，能实现用python语言完成大规模数据计算的，核密度估计。要求能形成可视化结果，最好有检验指标。

1. Gaussian Kernel Density Estimation
2. Kernel Density Estimation using Epanechnikov Kernel Function
3. Kernel Density Estimation using Silverman’s Rule of Thumb
4. Kernel Density Estimation using Cross-Validation
5. Non-Parametric Unsupervised Density Estimation using Voronoi Tessellation
6. Non-Parametric Supervised Density Estimation using Nearest Neighbor
7. Kernel Density Estimation using Gradient-Based Techniques
8. Bayesian Kernel Density Estimation
9. Mixture-Based Kernel Density Estimation
10. Multivariate Kernel Density Estimation

These methods can be implemented in Python using libraries like numpy, scipy and sklearn. To visualize the results, libraries like matplotlib and seaborn can be used. Evaluation metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) can be used to check the accuracy of the density estimation.