Monte Carlo simulation is a powerful method that uses probability distributions to model and simulate various types of systems. By applying controlled randomness, it explores the range and likelihood of possible outcomes within a scenario or system, producing results that vary in each run.

Learn two types of Monte Carlo simulations: stochastically (randomly) varying initial conditions input into a deterministic model and fixed initial conditions input into a stochastic model. In both, randomness is generated using probability distributions selected and parameterized to model the actual variability present in the scenario or system being modeled.

What You Will Learn:

• Definitions and distinctions between the two types of Monte Carlo Simulation
• Step-by-step procedures for implementing each type
• Source and analysis of randomness in Monte Carlo Simulation
• Implementation methods demonstrated through increasingly sophisticated examples
• Application of basic experimental design principles to Monte Carlo Simulation

This course is included in the Modeling and Simulation Certificate.

Earn 1.4 Continuing Education Units (CEUs).