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Course Detail

Course Name Probability Theory and Random Processes
Course Code 19MAT205
Program B. Tech. in Computer and Communication Engineering
Semester Three
Year Taught 2019


Module I

Review of probability concepts – conditional probability- Bayes theorem.
Random Variable and Distributions: Introduction to random variable – discrete and continuous random variables and its distribution functions- mathematical expectations – moment generating function and characteristic function.

Module II

Binomial, Poisson, Geometric, Uniform, Exponential, Normal distribution functions (moment generating function, mean, variance and simple problems) – Chebyshev’s theorem.

Module III

Stochastic Processes:
General concepts and definitions – stationary in random processes – strict sense and wide sense stationary processes – autocorrelation and properties- special processes – Poisson points, Poisson and Gaussian processes and properties- systems with stochastic inputs – power spectrum- spectrum estimation, ergodicity –Markov process and Markov chain, transition probabilities, Chapman Kolmogrov theorem, limiting distributions classification of states. Markov decision process


  • Douglas C. Montgomery and George C. Runger, Applied Statistics and Probability for Engineers, (2005) John Wiley and Sons Inc.
  • A. Papoulis, and Unnikrishna Pillai, “Probability, Random Variables and Stochastic Processes”, Fourth Edition,
    McGraw Hill, 2002.


  • J. Ravichandran, “Probability and Random Processes for Engineers”, First Edition, IK International, 2015.
  • Scott L. Miller, Donald G. Childers, “Probability and Random Processes”, Academic press, 2012.

Evaluation Pattern

Assessment Internal External
Periodical 1 (P1) 15
Periodical 2 (P2) 15
*Continuous Assessment (CA) 20
End Semester 50
*CA – Can be Quizzes, Assignment, Projects, and Reports.

Objectives and Outcomes


  • To understand the concepts of basic probability and random variables.
  • To understand some standard distributions and apply to some problems.
  • To understand the concepts of random process, stationarity and autocorrelation functions.
  • To understand markov process and markov chain and related concepts.

Course Outcomes

  • CO1: Understand the basic concepts of probability and probability modeling.
  • CO2: Gain knowledge about statistical distributions of one and two dimensional random variables and correlations
  • CO3: Understand the basic concepts of stochastic processes and the stationarity.
  • CO4: Understand the purpose of some special processes
  • CO5: Gain knowledge about spectrum estimation and spectral density function

CO – PO Mapping

CO1 3 2 3
CO2 3 3 3
CO3 3 3 2
CO4 3 3 2
CO5 2 2 2

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