Course: Monte-Carlo Simulations
ECTS points: 6 ЕКТС
Number of classes per week: 3+0+0+3
Lecturer: Prof. Dr. Aneta Buchkovska
Course Goals (acquired competencies): The student will be able to efficiently apply the Monte Carlo method for creative solving problems related to data analysis and interpretation of statistical models in a different context. The student will be able to clearly express the assumptions on which certain conclusions are based, using the Monte Carlo method with which the assumptions are systematically and critically examined using an analytical approach. He / she will be able to optimize with the help of stochastic simulation.
• Monte Carlo experiments. Evaluation using the arithmetic mean of a sample, estimating the dispersion of the sample, generating random numbers with P, generating quasi-random numbers with a uniform and uneven distribution.
• Generate discrete and continuous random variables. Statistical analysis of simulated data. Techniques for reducing dispersion. Butttrapping method
• Simulation of random processes. Generating trajectories of Markov processes; Monte Carlo Markov chains.
• Computer intensive techniques for assessing standard error, confidence intervals, hypothesis testing and prediction error
• Stochastic Optimization; Stochastic simulation approximation methods, gradient grading, etc.
|Sheldon M.Ross||Simulation (4th ed.)||Elsevier Academic Press||2006|
|James E. Gentle||Random Number Generation and Monte Carlo Methods||Springer||2003|