Title : Probability Foundations for Electrical Engineers
Course No : EE3110
Credits : 4
Prerequisite :

Syllabus :

  • Introduction to Probability:  Sets, Events, Axioms of Probability, Conditional Probability and Independence,  Bayes Theorem and MAP Decision Rule
  • Random Variables: Definitions, Cumulative Distribution Functions, mass and density functions, joint and conditional distributions, Functions of Random Variables
  • Expectations: Mean, Variance, Moments, Correlation, Chebychev and Schwarz Inequalities, Moment-generating and Characteristic Functions, Chernoff Bounds,  Conditional Expectations
  • Random Vectors:  Jointly Gaussian random variables, Covariance Matrices, Linear Transformations, Diagonalization of Covariance Matrices
  • Random Sequences: Sequences of independent random variables, correlation functions, wide-sense stationary sequences, LTI filtering of sequences
  • Law of Large Numbers,  Central Limit Theorem

Text Books :

  • Stark and Woods: Probability  and Random Processes with Applications to Signal Processing, 3rd ed 2002, Pearson Education

References :