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Course Detail

Course Name   Stochastic Process
Course Code 23DLS633
Credits 3

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.

Text Books

1. J. Ravichandran, “Probability and Random Processes for Engineers”, First Edition, IK International, 2015
2. Douglas C. Montgomery and George C. Runger, Applied Statistics and Probability for Engineers, (2005) John Wiley and Sons Inc.

Reference Books

1. A. Papoulis, and Unnikrishna Pillai, “Probability, Random Variables and Stochastic Processes”, Fourth Edition, McGraw Hill, 2002.
2. Scott L. Miller, Donald G. Childers, “Probability and Random Processes”, Academic press, 2012.

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