Other university all, Electronics and Communication engineering , Probability Theory and Stochastic Processes Syllabus

EC113 Probability Theory and Stochastic Processes

3 Credits

1. Sets and set operations; Probability space; Conditional probability and Bayes theorem; Combinatorial probability and sampling models. 

2. Discrete random variables, probability mass function, probability distribution function, example random variables and distributions; Continuous random variables, probability density function, probability distribution function, example distributions;

3. Joint distributions, functions of one and two random variables, moments of random variables; Conditional distribution, densities and moments; Characteristic functions of a random variable; Markov, Chebyshev and Chernoff bounds. 

4. Random sequences and modes of convergence (everywhere, almost everywhere, probability, distribution and mean square); Limit theorems; Strong and weak laws of large numbers, central limit theorem.

5. Random process. Stationary processes. Mean and covariance functions. Er-godicity. Transmission of random process through LTI. Power spectral density, Markov chain and Markov processes.

Sl. No. Name of Authors / Books /Publishers
1 “Probability and Random Processes with Applications to Signal Processing,” H. Stark and J. Woods, Third Edition, Pearson Education
2 “Probability, Random Variables and Stochastic Processes”, A.Papoulis and S. Unnikrishnan Pillai, Fourth Edition, McGraw Hill.
3 “Introduction to Probability Theory with Stochastic Processes”, K. L. Chung, Springer International