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    Stochastic Processes Course Fall 1399: Instructor: TA: Office/Email: Office Hours: Class Time: Class Location: Textbooks

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    Ms Aghamiri
    This document outlines the syllabus for a Stochastic Processes course taught in the fall of 1399. The instructor is Shahrokh Farahmand and the TA is Amir Andalib. The class will meet on Saturdays, Mondays, and Wednesdays from 9:00-10:30 am online via the LMS. The grading breakdown and topics to be covered include introduction to probability, random variables, properties of random variables, random vectors, random sequences, random processes, and advanced topics such as ergodicity and Karhunen-Loeve expansion.

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    Stochastic Processes Course Fall 1399: Instructor: TA: Office/Email: Office Hours: Class Time: Class Location: Textbooks

    Uploaded by

    Ms Aghamiri
    31 views2 pages
    This document outlines the syllabus for a Stochastic Processes course taught in the fall of 1399. The instructor is Shahrokh Farahmand and the TA is Amir Andalib. The class will meet on Saturdays, Mondays, and Wednesdays from 9:00-10:30 am online via the LMS. The grading breakdown and topics to be covered include introduction to probability, random variables, properties of random variables, random vectors, random sequences, random processes, and advanced topics such as ergodicity and Karhunen-Loeve expansion.

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    This document outlines the syllabus for a Stochastic Processes course taught in the fall of 1399. The instructor is Shahrokh Farahmand and the TA is Amir Andalib. The class will meet on Saturdays, Mondays, and Wednesdays from 9:00-10:30 am online via the LMS. The grading breakdown and topics to be covered include introduction to probability, random variables, properties of random variables, random vectors, random sequences, random processes, and advanced topics such as ergodicity and Karhunen-Loeve expansion.

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    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    31 views2 pages

    Stochastic Processes Course Fall 1399: Instructor: TA: Office/Email: Office Hours: Class Time: Class Location: Textbooks

    Uploaded by

    Ms Aghamiri
    This document outlines the syllabus for a Stochastic Processes course taught in the fall of 1399. The instructor is Shahrokh Farahmand and the TA is Amir Andalib. The class will meet on Saturdays, Mondays, and Wednesdays from 9:00-10:30 am online via the LMS. The grading breakdown and topics to be covered include introduction to probability, random variables, properties of random variables, random vectors, random sequences, random processes, and advanced topics such as ergodicity and Karhunen-Loeve expansion.

    Copyright:

    © All Rights Reserved
    Download as PDF, TXT or read online from Scribd
    Download as pdf or txt
    of 2

    Stochastic Processes Course

    Fall 1399

    Instructor: Shahrokh Farahmand


    TA: Amir Andalib
    Office/Email: 4th Floor of EE Building, Room 431/sha.farahmand@gmail.com
    Office Hours: Saturdays and Mondays 10:30 -12:30
    Class Time: Saturdays, Mondays, and Wednesdays 9:00 – 10:30
    Class Location: Online via LMS
    Textbooks:
    1) “Probability and Random Processes with Applications to Signal Processing,” by Henry Stark and John
    Woods, 3rd Edition, 2002.
    2) “Probability, Random Variables, and Stochastic Processes,” by Athanasios Papoulis and S.
    Unnikrishna Pillai, 4th Edition, 2002.

    Grading:
    1) Final Exam: 5 / 20
    2) Midterm Exam: 3 / 20
    3) Homework: 4 /20
    4) Thursday Quizzes: 8 / 20 (At least 8 Quizzes)

    Syllabus:
    1) Introduction to probability
     Probability space and Kolmogorov axioms
     Joint and conditional probabilities
     Bayes’ theorem
     Bernoulli, Binomial, and Poisson distributions

    2) Random variables (RVs)


     CDF / PDF
     Important densities
     Continuous, discrete, mixed RVs
     Condition/joint distributions

    3) Properties of RVs
     Functions of RVs
     Expectation and moments
     Independent and uncorrelated RVs
     Jointly Gaussian RVs
     Schwarz, Markov, Chebyshev inequalities
     Moment generating function, characteristic function
     Chernoff bound

    4) Random vectors
     Joint CDF/PDF
     Expectations and covariance matrices
     Properties of covariance matrices
     Multi-dimensional Gaussian
     Characteristic functions of random vectors

    5) Random sequences (RSs)


     Definitions
     Autocorrelation functions
     RSs and LTI systems
     Stationarity/Wide sense stationarity
     Power spectral density
     Markov random sequences /Markov chains
     Vector random sequences
     Convergence of RSs
     Martingales and their convergence

    6) Random processes (RPs)


     Definitions
     Autocorrelation functions
     Poisson counting processes
     Wiener processes or Brownian motion
     Markov random processes
     Continuous-time LTI systems and RPs
     Stationarity/Wide sense stationarity
     Power spectral density
     Periodic and cyclostationary processes
     Vector processes and state equations

    7) Advanced topics
     Ergodicity
     Karhunen-Loeve (KL) expansion
     Representation of bandlimited and periodic processes

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