All code related to this post is accessible at https://github.com/pierresegonne/VINF
The Posterior Distribution
Probability theory is key to quantify uncertainty in any phenomenon and is therefore essential to the development of thinking machines. Specifically, probabilistic models describe how the interactions between observed variables x, latent (in the sense of unobserved) variables z and parameters 𝜃 can generate useful information about target variables. In most cases, the knowledge of the posterior distribution p(z, 𝜃|x) is crucial. For example it allows to compute the marginal likelihood of a new observation x’,
Python, together with Matplotlib allow for easy and powerful data visualisation. It was originally developed for 2D plots, but was later improved to allow for 3D plotting. Furthermore, an animation module also allows for dynamical plotting. That is our goal today: Animate a scatter plot in 3D with Matplotlib.
Generate Data
Let’s first generate some dummy data. We want our data-structure to consist of a list of arrays of positions of our animated points (also called elements). Each element of the list being the positions at a different iteration.
Introduction
Working as a software engineer I was faced with the daunting task of creating a large collection of forms while using React. After several tries and improvements I adopted several design principles that I will be presenting in this tutorial. They greatly helped me in my task and I feel that sharing them could benefit other peoples creating forms in React.
Before presenting these principles, I would like to discuss quickly the tools/frameworks I used and thus explain how their features influenced the emergence of the principles I will be presenting.
I recommend being familiar with react to understand this…
Pierre Segonne
MSc Student at DTU & CentraleSupélec — Data Scientist at Tomorrow — Building a data driven sustainable future
