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13E053SSE - Stochastic Systems and Estimation

Course specification
Course title Stochastic Systems and Estimation
Acronym 13E053SSE
Study programme Electrical Engineering and Computing
Module Signals and Systems
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
    ESPB 6.0 Status mandatory
    Condition none
    The goal Introduce students to tools for modeling and analysis of stochastic processes, and to procedures for parametric estimation.
    The outcome Students will be enabled to apply different methods for the analysis of random processes. They will also gain the necessary theoretical and practical skills which will enable them to properly apply various methods for estimating the parameters of probabilistic models.
    Contents
    Contents of lectures Basic probability, random processes: stationarity and ergodicity, white processes, spectral representation, linear filtration, spectral factorization. Estimation theory: minimum variance unbiased estimator, Cramer-Rao lower bound, maximum likelihood, Bayesian approach, Wiener and Kalman filters.
    Contents of exercises Practical implementations of algorithms covered in lectures are demonstrated in class. Through homework assignments, students are required to implement and apply several algorithm for estimation and analysis of random processes using Python and Matlab/Octave.
    Literature
    1. Fundamentals of Stochastic Signals, Systems and Estimation Theory with Worked Examples, B. Kovačević, Ž. Đurović, Academic Mind, Belgrade, 1999. (Original title)
    2. Steven M. Kay, "Fundamentals of statistical signal processing, volume I: Estimation theory", 1993 (Original title)
    3. Steven M. Kay, "Intuitive probability and random processes using Matlab", 2006 (Original title)
    Number of hours per week during the semester/trimester/year
    Lectures Exercises OTC Study and Research Other classes
    3 2
    Methods of teaching 45 hours of lectures + 30 hours of auditory exercises
    Knowledge score (maximum points 100)
    Pre obligations Points Final exam Points
    Activites during lectures 0 Test paper 0
    Practical lessons 10 Oral examination 60
    Projects
    Colloquia 30
    Seminars 0