Correlation vs Mutual Information

This post is based on a (very small) part of the (dense and technical) paper Fooled by Correlation by N.N. Taleb, found at (1)

Notes on the main ideas in this post are available from Universidad de Cantabria, found at (2)

The aims of this post are to 1) introduce mutual information as a measure of similarity and 2) to show the nonlinear relationship between correlation and information my means of a relatively simple example

Introduction

A significant part of Statistical analysis is understanding how random variables are related – how much knowledge about the value of one variable tells us about the value of another. This post will consider this issue in the context of Gaussian random variables. More specifically, we will compare- and discuss the relationship between- correlation and mutual information.

Mutual Information

The Mutual Information between 2 random variables is the amount of information that one gains about a random variable by observing the value of the other.…

By | March 28th, 2020|English, Level: intermediate, Uncategorized|0 Comments

The Res-Net-NODE Narrative

This is second post in the blog series, and it is meant to give a broad narrative of the content for next two blog posts. Like the previous post, it will be more of an overview, but the two posts that will follow it will unpack and discuss deeply whatever appears in this one and slightly more.

Humble Beginnings: Ordinary Differential Equations

The story begins with differential equations. Consider f such that f:[0,T]\times \mathbb{R}^n\to \mathbb{R}^n is a continuous function. We can construct a rather simple differential equation given this in the following way. We let

\begin{cases}  {y'(t)}=f(t,y(t))\\  y(0)=y_0\in \mathbb{R}^n  \end{cases}

A solution to this system is a continuous map that is defined in the neighbourhood of t=0 such that this map satisfies the differential equation.

Ordinary differential equations are well-studied, and we know that, for example, a solution to the given differential equation will exist whenever the function f satisfies the following:

(\forall x,y\in \mathbb{R}^n)(\exists C>0 (\in \mathbb{R}))(||f(t,y)-f(t,x)||\leq C||y-x||)

This property is known as Lipschitz continuity. A function that satisfies this condition is said to be Lipschitz.…

By | March 18th, 2020|Uncategorized|Comments Off on The Res-Net-NODE Narrative

Deep Q Learning, Briefly!

This blog post is a direct translation of a talk that was given by the author on the 17th of February 2020. The ideas was to very briefly introduce Deep Q-Learning to an audience that was familiar with the fundamental concepts of reinforcement learning. If the person reading this is not familiar with these basics, then a very great introduction can be found here: An Introduction to Reinforcement Learning. Without the additional details from the talk, one will note that this post is rather brief, and should really be used as a tool to gain an overview for the method or a gateway to relevant resources. This will not the case for posts later in the series, because the intention is to deal more with the mathematical aspect of reinforcement learning.

Basic Reinforcement Learning Notions

The idea behind reinforcement learning is that there is an agent that interacts with the environment in order to achieve a certain task.…

By | March 18th, 2020|Uncategorized|0 Comments

Curves for the Mathematically Curious – an anthology of the unpredictable, historical, beautiful and romantic, by Julian Havil – a review

NB I was sent this book as a review copy.

What a beautiful idea. What a beautiful book! In studying mathematics, one comes across various different curves while studying calculus, or number theory, or geometry in various forms and they are asides of the particular subject. The idea however of flipping the script and looking at curves themselves and from them gaining insight into: statistics, combinatorics, number theory, analysis, cryptography, fractals, Fourier series, axiomatic set theory and so much more is just wonderful.

This book looks at ten carefully chosen curves and from them shows how much insight one can get into vast swathes of mathematics and mathematical history. The curves chosen are:

  1. The Euler Spiral Рan elegant spiral which leads to many other interesting parametrically defined curves
  2. The Weierstrass Curve – an everywhere continuous but nowhere differentiable function
  3. Bezier Curves – which show up in computer graphics and beyond
  4. The Rectangular Hyperbola – which leads to the investigation of logarithms and exponentials
  5. The Quadratrix of Hippies – which are tightly linked to the impossible problems of antiquity
  6. Peano’s Function and Hilbert’s Curve – space filling curves which lead to a completely flipped understanding of the possibilities of infinitely thin lines
  7. Curves of Constant Width – curves which can perfectly fit down a hallway as they rotate.
By | March 15th, 2020|Book reviews, Reviews, Uncategorized|0 Comments