> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/foundation-knowledge/probability.md).
# Probability
### **PDF (PROBABILITY DENSITY FUNCTION)**
1. [**Tutorial in scipy**](https://oneau.wordpress.com/2011/02/28/simple-statistics-with-scipy/)
2. [**Array-based tutorial in python with PDF and KDE**](http://firsttimeprogrammer.blogspot.co.il/2015/01/how-to-estimate-probability-density.html)
3. [**Summary of univariate distribution including pdf methods**](https://www.johndcook.com/blog/distributions_scipy/)
### **Kernel Density Estimation**
**This** [**tutorial**](https://mglerner.github.io/posts/histograms-and-kernel-density-estimation-kde-2.html?p=28) **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**](https://github.com/Daniel-B-Smith/KDE-for-SciPy) **of KDE than SCIPY**
**How to use KDE? A** [**tutorial**](http://pythonhosted.org/PyQt-Fit/KDE_tut.html) **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** ](https://www.youtube.com/watch?v=gPWsDh59zdo)
2. **Non parametric** [**Kernel Regression Estimation**](https://www.youtube.com/watch?v=ncF7ArjJFqM)
3. **Non parametric** [**Sieve Estimation**](https://www.youtube.com/watch?v=cqecz-DL-jI)
4. [**Semi- nonparametric estimation**](https://www.youtube.com/watch?v=G1N53K530To)
[**Udacity Video Tutorial**](https://www.youtube.com/watch?v=MEP35FcrQGs\&list=PLAwxTw4SYaPn-ttWkPiUL7NP3lLRdUniJ\&index=80) **- pretty good**
1. **IMPORTANT:** [**Comparison and benchmarks of various KDE algo’s**](https://jakevdp.github.io/blog/2013/12/01/kernel-density-estimation/)
2. [**Histograms and density plots**](https://towardsdatascience.com/histograms-and-density-plots-in-python-f6bda88f5ac0)
3. [**SK LEARN**](http://scikit-learn.org/stable/modules/density.html#kernel-density-estimation)
4. [**Gaussian KDE in scipy, version 2**](https://www.youtube.com/watch?v=MEP35FcrQGs\&list=PLAwxTw4SYaPn-ttWkPiUL7NP3lLRdUniJ\&index=80)
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