EE511- Simulation Methods for Stochastic systems at University of Southern California
This course complements understanding of probability theory with a project‐oriented investigation of random systems and stochastic simulation methods. You will learn practical skills to aid you in analysis of random phenomena. The course begins with basic methods that underlie stochastic computational applications. Early projects prepare you to put randomness to work with more complex optimization problems including Expectation Maximization and Markov chain Monte Carlo (MCMC) methods.
The repository consists of projects with a detailed documentation of results and visualizations whenever needed. The topics on which the projects presented are:
- Coin flips.
- Samples and statistics.
- Integrals and Intervals.
- Markov Chain and discrete events.
- Continuous Sampling.
- Expectation Maximization.
- Markov Chain Monte Carlo.