STAT 4345 Introduction to Simulation

The course covers random number generation; generating discrete and continuous random variables; generating multivariate normally distributed vectors; Monte Carlo simulation experiments; Monte Carlo integration and variance reduction; Monte Carlo methods in Statistical Inference; Resampling Techniques such as Jack-Knife and Boot Strap; Simulation of Stochastic Processes. Statistical software like R and Python will be used during classes.

Credits

3

Prerequisite

STAT 2336 and STAT 3338 both with a grade of "C" or better.

Schedule Type

Lecture

Grading Basis

Standard Letter (A-F)

Administrative Unit

School of Mathematical and Statistical Sciences