probability.md

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.

• Supposedly a better implementation of KDE than SCIPY

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