Math
This is my extended brain for math
Understanding the Directional Derivative and the Gradient
1 Introduction Understanding how functions change in different directions is crucial in many fields. For example in the context of neural networks where gradients are used to update weights during
Maximum Likelihood Estimation
The method of maximum likelihood estimation allows to estimate point parameters for a given distribution underlying some observed data. Let’s look at an example to understand what this means:Imagine you
How to create a random variable with a Beta distribution from scratch, using only Uniform random variables
You can use software, like scipy.stats.beta if you want to sample from a Beta distribution. But you can also create a Beta distribution yourself — from scratch. The only thing you need
Which envelope should you choose?
A detailed solution for a problem about total probability
Math, Probability and Statistics
Variance, Covariance, Autocovariance…what?
This article aims to distinguish between Variance, Covariance, Correlation, Autocovariance, and Autocorrelation. Furthermore there will be a numerical discrete example for computing the Autocorrelation in python. This article will not