Stochastic Simulation Algorithms

(3 Credit Hours) · Undergraduate

This course will develop practical techniques for the simulation of stochastic systems. Stochastic models (as opposed to deterministic) do not produce the same outcome from identical input parameters. Extreme weather, the outcome of sporting events, or the fluctuation of asset prices observed in the stock market are examples of natural and human systems that we wish to understand, but that are governed largely by stochastic (or random) processes. This course will develop a suite of computational methods, collectively called Stochastic Simulation Algorithms (SSAs), for understanding the range, or distribution, of outcomes that can arise from stochastic models. The course will emphasize practical implementation and benchmarking of algorithms in Python.