Statistics and elections (links)

• https://projecteuclid.org/euclid.aoas/1458909907
• https://kobak.livejournal.com/111535.html
• Integer percentages as electoral falsification fingerprints. The Annals of Applied Statistics. Volume 10, Number 1 (2016), 54-73. (checked by F.S.)
• https://arxiv.org/pdf/1205.0741.pdf
• http://www.pnas.org/content/pnas/109/41/16469.full.pdf
• 
  

OCaml and Julia

Implementing the Node type

Parameter free global optimization (?)

• Global optimization of Lipschitz functions by Cédric Malherbe, Nicolas Vayatis (2017)
• Blog post: "A Global Optimization Algorithm Worth Using"
• reddit

Test LaTeX equations via Emacs in Julia mode

∑xᵢ

Also woks in Atom

\Psi tab \^i tab etc

Ψⁱᵦ

Great statistics course from Harvard University

Statistics 110: Probability

Lectures 24, 25 are about the Gamma distribution. At the beginning of the lec. 25 there is a very good example about modelling waiting times in the bank / post-office.

If you are in the line of 5 people and the waiting time for individual customer is a Poisson process (Exponential distribution) then your's waiting time will be given by Γ(t | λ = 5, rate = 1)

Data Science

(Not a big insight, but anyway))

Disciplines which are in the heart of Data Science:

• Statistics and Statistical learning theory
• Linear Algebra (LA) 
• Algorithms and data structures
• Optimization

They form a theoretical background necessary to understand how most of the Machine Learning/Data Mining algorithms work. Why do you need to know how internals work? Because most of the ML algorithms are "leaky abstractions" [1],[2].

The Data Mining is a process of finding the patterns in the data. LA is not only the tool, but it inherently contains elements of pattern recognition. For example the process of matrix factorization reveals patterns in the column / row space of the matrix. 

- [1] "Yes you should understand backprop" by Andrej Karpathy

- [2] HN discussion: "Backpropagation is a leaky abstraction"

Two papers

complementing each other:

• "Statistical Modeling: The Two Cultures" by Leo Breiman
• "To Explain or to Predict?" by Galit Shmueli