Explore stochastic simulation modeling in this 55-minute lecture from MIT's Computational Thinking Spring 2021 course. Dive into Julia programming language features and learn about individual-based ("microscopic") models. Discover techniques for modeling time to success or failure, and visualize component failure. Master string interpolation, including HTML examples in Pluto. Delve into mathematical concepts like Bernoulli random variables and their implementation in Julia. Run stochastic simulations and gain insights into the time evolution of the mean through intuitive derivation. Enhance your computational thinking skills and expand your knowledge of advanced modeling techniques in this comprehensive lecture.
Overview
Syllabus
Introduction.
Julia features.
Individual-based ("microscopic") models.
Modelling time to success (or time to failure).
Visualizing component failure.
String interpolation.
String interpolation (HTML example in Pluto).
Math: Bernoulli random variables.
Julia: Make it a type!.
Running the stochastic simulation.
Time evolution of the mean: Intuitive derivation.
Taught by
The Julia Programming Language
