**1** |
Introduction to the course and the term project. Definition of system, classification of systems and definition of Monte Carlo Simulation.
System analysis, description of the problem, components of system, state of system and system event
The algorithm of simulation models. The steps of a simulation study.
Probability and statistics. Analysis of random input variables: Correlogram, scatter diagram, runs tests.
Analysis of random input variables: Histogram, PP and QQ chart
Analysis of random input variables: Goodness of fit tests: Chi-square test, KS test
Random numbers, random variate. Inverse transfer functions.
MIDTERM EXAM
Output Analysis: Confidence interval. Terminating simulations.
Output Analysis: Warm-up period, autocorrelation. Non-terminating simulations.
Output Analysis: Examples.
Variance Reduction Techniques: Indirect measures, control variates.
Variance Reduction Techniques: Common random numbers, antithetic random numbers.
Validation and verification of simulation models. |