Probability

PDF (PROBABILITY DENSITY FUNCTION)

  1. Tutorial in scipy

  2. Array-based tutorial in python with PDF and KDE

  3. Summary of univariate distribution including pdf methods

Kernel Density Estimation

This tutorial actually explains why we should use KDE over a Histogram, it explains the cons of histograms and how KDE helps solve some issue that we usually encounter in ‘Sparse’ histograms where the distribution is hard to figure out.

How to use KDE? A tutorial about kernel density and how to use it in python. Has several good graphs and shows use cases.

Video tutorials about Kernel Density:

  1. KDE

  2. Non parametric Kernel Regression Estimation

  3. Non parametric Sieve Estimation

  4. Semi- nonparametric estimation

Udacity Video Tutorial - pretty good

  1. IMPORTANT: Comparison and benchmarks of various KDE algo’s

  2. Histograms and density plots

  3. SK LEARN

  4. Gaussian KDE in scipy, version 2

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