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  1. © 2025 National Institute of Technology Kurukshetra. All Rights Reserved.

    CSPE322 Stochastic Processes and Queuing Theory

    Prerequisites:

    No prerequisites for this course

    Course Nature: PE
    Objectives:
  1. Provides an essential base for mathematical modeling used to solve problems in pattern recognition and machine learning.
  2. Used in the research of various science and engineering problems.
    3. Similar Courses:

    Course Coordinator

    Dr. Sushil Kumar Madan
    Dr. Sushil Kumar Madan

    Professor

    skmadan@nitkkr.ac.in

    9416292144

    Content

    ChariotHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorse

    Outcomes

    ChariotHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorse
    1. Develop an understanding of the basic concepts of Random Processes, Expectations, and Parameter Estimation.
    2. Apply Stochastic processes to science and engineering problems and applications.
    3. Calculate the n-step transition probabilities for any Markov chain and understand the birth and death of processes.
    4. Apply Markov chain and birth-death processes to real-life problems.
    5. Develop an understanding of various queuing systems.
    ChariotHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorseHorse

    Reference Books

    1. A. Populis and S.U. Pillai, 'Probability, Random Variables, and Stochastic Process', 4th Edition, McGraw-Hill.

    2. S. Palaniammal, 'Probability and Queueing theory.' Prentice Hall of India.

    3. V. Sundarapandian, 'Probability, Statistics and Queueing Theory.' Prentice Hall of India.