| Course Name | Probability Theory and Random Processes |
| Course Code | 19MAT205 |
| Program | B. Tech. in Computer and Communication Engineering |
| Semester | Three |
| Year Taught | 2019 |
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.
Stochastic Processes:
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
Evaluation Pattern
| Assessment | Internal | External |
| Periodical 1 (P1) | 15 | – |
| Periodical 2 (P2) | 15 | – |
| *Continuous Assessment (CA) | 20 | – |
| End Semester | – | 50 |
| *CA – Can be Quizzes, Assignment, Projects, and Reports. | ||
Objectives
Course Outcomes
CO – PO Mapping
| PO/PSO/CO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 |
| CO1 | 3 | 2 | 3 | – | – | – | – | – | – | – | – | – | – | – |
| CO2 | 3 | 3 | 3 | – | – | – | – | – | – | – | – | – | – | – |
| CO3 | 3 | 3 | 2 | – | – | – | – | – | – | – | – | – | – | – |
| CO4 | 3 | 3 | 2 | – | – | – | – | – | – | – | – | – | – | – |
| CO5 | 2 | 2 | 2 | – | – | – | – | – | – | – | – | – | – | – |
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