Introduction to Statistical Signal Processing


Stanford School of Engineering



Understand how random processing signals are characterized and how operations change signals require a combination of theory and application. This course introduces the concept of probability and sampling of signal processing with a wide variety of applications and mathematical approaches.

As the concepts of signal processing become clear, learn from increasingly complex examples of random processes. Practice using examples of commonly encountered processes, properties and calculations drawn from communications, signal processing, computer networks, circuits, and devices, among other areas.



EE178 and linear systems; Fourier transforms at the level of EE102A, EE102B or EE261 .

Topics include

  • Random vectors and processes
  • Convergence and limit theorems
  • IID, independent increment, Markov, and Gaussian random processes
  • Autocorrelation and power spectral density
  • Mean square error estimation, detection, and linear estimation

Note on Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate certificate homepage.