# Probability

![by en.wikipedia.org](https://lh6.googleusercontent.com/9S5NKsR3t2sboezqW5ehsAxbjt4JE7SUaNB9RTvhQnW4WgaeOROOSYrld5hfXDA2bApG_3mwtgUEq6fbrwBKzceevdNwNppmWRoQLgnQknVBHZ7O15xlQP9IzYdZBIKAMa9yLfzz)

![by en.wikipedia.org](https://lh4.googleusercontent.com/p-jCke8I8OrR2T_SGXui54kTIJTWuE3ZguFv1lckkcZktBbZTMEUg9Ay0kPIj_yzM0XM9kt9QJyt-m0tI7ntQNPVAJzv21o5-1DGY1l7trnBjYmAaxbjSVhTnFKd9nVpzVddGD0k)

![by en.wikipedia.org](https://lh5.googleusercontent.com/uPYrn2f4iHu_DmAsb2iNqVhBHWW45dM42RuUySlETPgdwEuqfBqmi2IAS2sPrSK_Jo-C3TIes5nhbrMy1EZA8vHgjphfT8izv1SIpARzqjfbuy86MUei1igeogo5t-8Xe9KWzYXw)

![by en.wikipedia.org](https://lh3.googleusercontent.com/vays0BSzI-zCZnLBLuoafnt0QRE25toMq449bgTsyp2vf23n6ZAi-ShaBDa73v-V_aonwcpdy6EPsEYbiW40z9F2rgickFYFuuEDo0VVdisAet4GZ0rlMGjBvtT4LeyYQ6F_Wb5A)

### **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**<br>

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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