Shanmuga

ES331 Probability and Random Processes 2018-19

Lectures - D Slot
Tutorial - G2 Slot
Lecture Hall - 7-208, Tutorial Halls - 7-202, 7-204
Instructor - Shanmuga, AB6-327A, PH 2453. E-mail: shanmuga@iitgn.ac.in
TAs - Rajendra Nagar rajendra.nagar@iitgn.ac.in, Aalok Gangopadhyay aalok@iitgn.ac.in, Sudhakar Kumawat sudhakar.kumawat@iitgn.ac.in, Indra Deep Mastan indra.mastan@iitgn.ac.in
Pre-requisite - Probability and Statistics (MA 202)

Course Content

Review of sets, fields and events, axioms of probability, probability space, conditional probability, independence, Bayes’ theorem and applications. Repeated trials, Bernoulli trials, discrete, continuous and mixed random variables, probability mass function, probability distribution and density functions.Examples of common random variables and density functions, conditional distributions and densities, functions of one and two random variables, moments and characteristic functions of random variables, mean, variance, correlation. Markov, Chebychev and Chernoff bounds, sequences of random variables, strong and weak law of large numbers, central limit theorem, linear mean square estimation and orthogonality principle, maximum likelihood and parameter estimation.

Random processes, strict and wide sense stationary processes, ergodic processes, bandlimited and periodic processes, random processes and linear systems, power spectral density. Noise processes, Wiener filtering, Kalman filtering, examples of random processes, Poisson process, Markov process.

Textbook

Kay, S. (2006). Intuitive Probability and Random Processes using MATLAB. Springer.

Reference Books

Bertsekas, D. P. & Tsitsiklis, J.N. (2002). Introduction to Probability. 2nd Edition. Athena Scientific.
Pishro-Nik, H. (2014). Introduction to Probability, Statistics and Random Processes.
Walrand, J. (2014). Probability in Electrical Engineering and Computer Science: An Application-Driven Course. Quorum Books.
Hsu, H. (2014). Schaum's Outline of Probability, Random Variables, and Random Processes. Third Edition. Schaum's Outline Series.
Gubner, J. A. (2006). Probability and Random Processes for Electrical and Computer Engineers. Cambridge University Press.
Stark, H., & Woods, J. W. (2011). Probability, Statistics, and Random Processes for Engineers. 4th Edition. Pearson.
Yates, R. D., & Goodman, D. J. (2014). Probability and Stochastic Processes. A Friendly Introduction for Electrical and Computer Engineers. Third Edition. Wiley.
Grimmett, G., & Welsh, D. (2014). Probability: An Introduction. 2nd Edition. Oxford University Press.
Grimmett, G., & Stirzaker, D. (2001). Probability and Random Processes. 3rd Edition. Oxford Univ Press.
Papoulis, A., & Pillai, S. U. (2002). Probability, Random Variables, and Stochastic Processes. 4th Edition. Tata McGraw-Hill Education.
Gallager, R. G. (2013). Stochastic processes: Theory for Applications. Cambridge University Press.
Leon Garcia, A. (2008). Probability, Statistics, and Random Processes For Electrical Engineering. 3rd Edition. Pearson.
Ibe, O. (2014). Fundamentals of Applied Probability and Random Processes. 2nd Edition. Academic Press.

Leisure Reading

Wheelan, C. (2013). Naked statistics: stripping the dread from the data. WW Norton & Company.
Silver, N. (2012). The signal and the noise: Why so many predictions fail-but some don't. Penguin.
Rosenthal, J. S. (2006). Struck by Lightning:: The Curious World of Probabilities. National Academies Press.
Mlodinow, L. (2009). The drunkard's walk: How randomness rules our lives. Vintage Books.
Wheelan, C. (2010). Naked Economics: Undressing the Dismal Science (Fully Revised and Updated). WW Norton & Company.
Kaplan, M., & Kaplan, E. (2006). Chances are–: adventures in probability. Penguin.
Holland, B. K. (2002). What are the chances?: voodoo deaths, office gossip, and other adventures in probability. JHU Press.

Grading

Programming Assignments - 20%
Quizzes - 20%
Mid Sem Exam - 20%
Final Exam - 40%

Expected Learning Outcomes

Probability and Random Processes is a core course for third year B.Tech Electrical Engineering and Computer Science and Engineering students. This course is subsequent to the course on Probability and Statistics. It forms the basis for many advanced courses such as Estimation and Detection Theory, Communication Systems, Computer Networks, Randomized Algorithms, Information Theory, Adaptive Signal Processing, Machine Learning, Optimal Control, Data-driven Applied Computer Vision. Though the course is prescribed as core course for EE & CSE students, applications used to emphasize various concepts would be drawn from multiple disciplines.

Contacting Instructor

Primary mode of contact will be to send an email and fix an appointment to meet. Queries may be posted in Google classroom.