mca Syllabus

Modeling and Simulation
Code: PGCS105F
Weekly Contact Hour: 3L
Credit: 3

Course Contents
Introduction to Probability theory, Random variables, commonly used continuous and discrete distributions. Introduction to Stochastic Process, Poisson process, Markov chains, steady stateand transient analysis. Psuedo random numbers: Methods of Generation and testing. Methods for generating continuous and discrete distributions. Methods for generating Poisson Process. Building blocks of Simulation, Data Structures and Algorithms. Introduction to Probabilistic modelling, Maximum Likelihood Variance
reduction techniques: antithetic variates, control variates, common random numbers, importance sampling. Analysis of Simulation results: confidence intervals, design of experiments Markov Chain Monte Carlo techniques

Books
1. Sheldon M. Ross: Introduction to Probability Models 7th Edition, Academic Press, 2002
2. Donald E. Knuth: The Art of Computer Programming - Volume 2: Semi Numerical Algorithms, 2nd Edition, PEARSON
3. Education, Reading MA, USA 2000
4. Sheldon M. Ross: Simulation 3rd Edition, Academic Press, 2002
5. M. Law and W. D. Kelton. Simulation Modeling and Analysis, 3rd Edition, McGrawHill, New York, USA, 1998
6. Raj Jain: The Art of Computer Systems Performance Analysis, John Wiley and Sons, New York, USA, 1991