Review of probability concepts – conditional probability- Bayes theorem. Random Variable and Distributions: Introduction to random variable – discrete and continuous random variables and its distribution functions- mathematical expectations – moment generating function and characteristic function.
Binomial, Poisson, Geometric, Uniform, Exponential, Normal distribution functions (moment generating function, mean, variance and simple problems) – Chebyshev’s theorem.
General concepts and definitions -stationary in random processes -strict sense and wide sense stationary processes – autocorrelation and properties- special processes – Poisson points, Poisson and Gaussian processes and properties- systems with stochastic inputs – power spectrum- spectrum estimation, ergodicity –Markov process and Markov chain, transition probabilities, Chapman Kolmogrov theorem, limiting distributions classification of states. Markov decision process.
Textbook / Reference
- Douglas C. Montgomery and George C. Runger, Applied Statistics and Probability for Engineers, (2005) John Wiley and Sons Inc.
- A. Papoulis, and Unni krishna Pillai, “Probability, Random Variables and Stochastic Processes”, Fourth Edition, McGraw Hill, 2002.
- J. Ravichandran, “Probability and Random Processes for Engineers”, First Edition, IK International, 2015.
- Scott L. Miller, Donald G. Childers, “Probability and Random Processes”, Academic press, 2012.
Evaluation Pattern 50:50 (Internal: External)
|Periodical 1 (P1)
|Periodical 2 (P2)
|*Continuous Assessment (CA)
|*CA – Can be Quizzes, Assignment, Projects, and Reports.