/Simulation-Methods

Coin flips, Samples and statistics, Integrals and Intervals, Markov Chain and discrete events, Continuous Sampling, Expectation Maximization, Markov Chain Monte Carlo.

Simulation-Methods

EE511- Simulation Methods for Stochastic systems at University of Southern California

Introduction:

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.