ECE 534: RANDOM PROCESSES FALL 2013 SYLLABUS

I. Review of Probability Theory (2 weeks)

  1. Basic axioms; probability space and measure; sigma algebras
  2. Conditional probability and independence
  3. Random variable; probability distribution and density
  4. Random vectors (multivariate random variables); independence; conditional distributions
  5. Functions of random variables and random vectors
  6. Expectation
  7. Conditional expectation and its properties

II. Sequences of Random Variables (2 weeks)

  1. Various notions of convergence
  2. Limit theorems
  3. Large deviations

III. Random Vectors and Minimum Mean Squared Error (MMSE) Estimation (2 weeks)

  1. Best linear MMSE estimators
  2. MMSE estimators
  3. Orthogonality principle
  4. Jointly Gaussian random variables and vectors
  5. Kalman filtering

IV. Random Processes (2 weeks)

  1. Continuous- and discrete-time random processes
  2. Stationarity and wide-sense stationarity (WSS)
  3. Second-order processes; mean and correlation function spectrum
  4. Markov processes and martingales; Gaussian, Wiener, and Poisson processes

V. Inference for Markov Processes (1 week)

  1. A bit of estimation theory
  2. The expectation-maximization (EM) algorithm
  3. Inference for hidden Markov models

VI. Dynamics of Countable-State Markov Models (1 week)

  1. Classification and convergence properties of countable-state Markov processes
  2. Models of queueing theory
  3. Stability criteria

VII. Calculus for Random Processes (2 weeks)

  1. Continuity of random processes; differentiation and integration
  2. Orthogonal representation of random processes (Karhunen-Loeve expansion)
  3. Ergodicity

VIII. Stationary Random Processes and Spectral Analysis (1 week)

  1. Power spectral density and its estimation
  2. Random processes through linear systems
  3. Spectral representation of random processes

IX. Minimum Mean Squared Error (MMSE) Estimation (1 week)

  1. MMSE estimation and linear MMSE estimation for random vectors (the orthogonality principle)
  2. Discrete- and continuous-time Kalman filter
  3. The Wiener filter; spectral factorization
  4. Some applications