Monte Carlo Simulation
Learn two types of Monte Carlo simulation: Stochastically (randomly) varying initial conditions are input to a deterministic model; and, fixed initial conditions are input to a stochastic model.
Andrew Davis
- Instructor
Offered by
Offered by
Description
Monte Carlo Simulation is a powerful method that uses probability distributions to simulate many types of systems.
Monte Carlo simulation uses randomness, applied in a controlled manner, to explore the range and likelihoods of possible outcomes inherent in a scenario or system and produces results that vary from run to run.
Learn two types of Monte Carlo simulation: Stochastically (randomly) varying initial conditions are input to a deterministic model; and, fixed initial conditions are input to 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.
- Online
- Intakes: TBA
What you will Learn
- Definitions of two different types of Monte Carlo Simulation
- Step-by-step procedures for each type
- Source and analysis of randomness in Monte Carlo Simulation
- Implementation methods used in several increasingly sophisticated examples
- Application of basic experimental design to Monte Carlo Simulation
Meet your Instructor
Andrew Davis earned a Bachelor of Science in Pure Mathematics from The University of Alabama and a Master of Science in Industrial and Systems Engineering from The University of Alabama in Huntsville.
He is currently working on his doctorate in the area of statistical modeling. In addition, he works as a statistician in the UAH Systems Management and Production Center on projects intended to
promote engineering education in rural areas.