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If the data is continuous, use kernel density estimation (KDE) instead of histograms to visualize the probability density, since KDE will give a smoother fit. A
by Bostonian 3y ago
If the data is continuous, use kernel density estimation (KDE) instead of histograms to visualize the probability density, since KDE will give a smoother fit. A similar idea is to fit a mixture of normals -- there are numerous R packages for this and sklearn.mixture.GaussianMixture in SciPy.
- vvanirudh 3y agoYep! The next post would be on Kernel density estimation -- wanted to start from histograms as they are still a useful tool in 1-D and 2-D density estimation, and you don't have to store the data either (unlike KDE)