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  • PDF (PROBABILITY DENSITY FUNCTION)
  • Kernel Density Estimation

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  1. Foundation Knowledge

Probability

PreviousProbability & StatisticsNextHypothesis Testing

Last updated 3 years ago

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PDF (PROBABILITY DENSITY FUNCTION)

Kernel Density Estimation

Video tutorials about Kernel Density:

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This 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 of KDE than SCIPY

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

Non parametric

Non parametric

- pretty good

IMPORTANT:

Tutorial in scipy
Array-based tutorial in python with PDF and KDE
Summary of univariate distribution including pdf methods
tutorial
implementation
tutorial
KDE
Kernel Regression Estimation
Sieve Estimation
Semi- nonparametric estimation
Udacity Video Tutorial
Comparison and benchmarks of various KDE algo’s
Histograms and density plots
SK LEARN
Gaussian KDE in scipy, version 2
en.wikipedia.org
en.wikipedia.org
en.wikipedia.org
en.wikipedia.org