Explore random walks and related concepts in this lecture from MIT's Computational Thinking Spring series. Dive into Julia programming language fundamentals before delving into Pascal's triangle and its connection to convolutions. Examine random walks as independent and identically-distributed random variables, and learn how to represent them as cumulative sums. Investigate the evolution of probability distributions over time and implement time evolution of probabilities. Follow along with timestamped sections covering introduction, Julia concepts, Pascal's triangle, convolutions, random walks, cumulative sums, and probability distribution evolution.
Overview
Syllabus
Introduction.
Julia concepts.
Pascal's triangle.
Convolutions build Pascal's triangle!.
Random walks: Independent and identically-distributed random variables.
Random walks as a cumulative sum.
Cumulative sum.
Evolving probability distributions in time.
Implementing time evolution of probabilities.
Taught by
The Julia Programming Language
