| Course Name | Stochastic Process |
| Course Code | 24ASD646 |
| Program | M.Sc. in Applied Statistics and Data Analytics |
| Credits | 3 |
| Campus | Coimbatore , Kochi |
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.
Course Outcomes:
CO1: To Understand the basic concepts of Stochastic Process and Stationarity and
Autocorrelation
CO2: To Understand the concepts of Special Processes
CO3: To Understand the concepts of Ergodicity and Power Spectral Density
CO4: To understand the concepts of Markov Chain and related results
CO-PO Mapping:
|
PO1 |
PO2 |
PO3 |
PO4 |
PO5 |
PO5 |
PO6 |
PO7 |
PO8 |
PO9 |
PO10 |
PO11 |
PO12 |
|
|
CO1 |
3 |
3 |
2 |
2 |
2 |
1 |
2 |
1 |
|||||
|
CO2 |
3 |
2 |
2 |
2 |
2 |
1 |
2 |
1 |
|||||
|
CO3 |
3 |
2 |
2 |
2 |
2 |
1 |
2 |
1 |
|||||
|
CO4 |
3 |
2 |
1 |
2 |
2 |
1 |
2 |
1 |
|||||
|
CO5 |
2 |
2 |
2 |
2 |
2 |
1 |
1 |
1 |
Text Books /Reference Books:
1) J. Ravichandran, “Probability and Random Processes for Engineers”, First Edition, IK
International, 2015
2) Roy D. Yates and David J. Goodman, “Probability and Stochastic Processes A Friendly
Introduction for Electrical and Computer Engineers”. Second Edition. John Wiley &
Sons.INC.
3) A. Papoulis, and Unnikrishna Pillai, “Probability, Random Variables and Stochastic
Processes”, Fourth Edition, McGraw Hill, 2002.
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