EE278: Introduction to Statistical Signal Processing

David Tse, Stanford University, Autumn 2014

Overall Contents

Law of large numbers and central limit theorem; random vectors and processes; Gaussian, Markov processes; stationarity; autocorrelation and power spectral density; minimum mean square error estimation, detection, linear estimation, Kalman and Wiener filtering; Cramer Rao bound; introduction to compressed sensing.

Lectures

  • Tue, Thu 11:00 AM - 12:15 PM at Hewlett 201. Note change in location.

Announcements

  • 10/21: Midterm exam will be held at Hewlett 200, on Oct 27 from 6pm to 8pm. Practice problems for the midterm.

  • 10/18: Homework 4 is out. It is due on Friday, 10/24.

  • 10/14: No lecture today. The make-up lecture will be in Hewlett 201, from 8:10 am to 9:00 am, Friday, 10/17.

  • 10/10: Homework 3 is out. It is due on Friday, 10/17.

  • 10/2: Homework 2 is out. It is due on Thursday, 10/9.

  • 9/29: Classroom changed to Hewlett 201.

  • 9/25: Homework 1 is out. It is due on Thursday, 10/2.