4 Ways to Measure Fat Tails with Python (+ Example Code)
Skills:
ML Maths Basics90%
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This is the 3rd video in a series about Power Laws and Fat Tails. In this video, I break down 4 ways we can quantify fat tails and share Python code analyzing real-world data.
๐น Series Intro: https://youtu.be/Wcqt49dXtm8
๐น Previous video: https://youtu.be/x5-IW1m3zPo
๐ฐ Read more: https://medium.com/towards-data-science/4-ways-to-quantify-fat-tails-with-python-10ce62c0ada1?sk=3aa9397cdd9f8acbd0fdf40d90c2cba5
๐ป GitHub Repo: https://github.com/ShawhinT/YouTube-Blog/tree/main/power-laws/3-quantifying-fat-tails
References
[1] Scipy Kurtosis: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.kurtosis.html#scipy.stats.kurtosis
[2] Scipy Moment: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.moment.html
[3] arXiv:1802.05495 [stat.ME]
[4] https://en.wikipedia.org/wiki/Log-normal_distribution
[5] Pham-Gia, T., & Hung, T. (2001). The mean and median absolute deviations. Mathematical and Computer Modelling, 34(7โ8), 921โ936. https://doi.org/10.1016/S0895-7177(01)00109-1
Intro - 0:00
Fat Tails - 0:45
4 Ways to Quantify Fat Tails - 2:02
Heuristic 1: Power Law Tail Index - 2:32
Heuristic 2: Kurtosis - 3:50
Heuristic 3: Log-normal's ฯ - 5:29
Heuristic 4: Taleb's ฮบ - 7:04
Example Code: Quantifying Fat Tails in Social Media - 11:44
What's next? - 22:29
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