Course outcomes
| CO1 Understand the concepts of stochastic process, markov chains and classifical of states and chains. |
| CO2. Understand the markov process with discrete state space as poisson process and its properties with related theorems. |
| CO3. Understand the markov process with continuous state space as wiener process and its properties. |
| CO4. Understand the renewal process and related theorems. |
| CO5. Understand the concepts of branching process and Bellman-Harris process. |
Random processes: General concepts and definitions – stationarity in random processes – strict sense and wide sense stationary processes – autocorrelation and properties- special processes – Poisson points, Poisson and Gaussian processes and properties , spectrum estimation , ergodicity, mean ergodicity, correlation ergodicity, Power spectrum density functions – properties, Markov process and Markov chain, transition probabilities, Chapman Kolmogrov theorem, limiting distributions classification of states.