Courses:

Course Info

• Course Number / Code:
• 6.432 (Spring 2004) 
• Course Title:
• Stochastic Processes, Detection, and Estimation 
• Course Level:
• Graduate 
• Offered by :
• Massachusetts Institute of Technology (MIT)
  Massachusetts, United States  
• Department:
• Electrical Engineering and Computer Science 
• Course Instructor(s):
• Prof. Alan Willsky
  Prof. Gregory Wornell 
• Course Introduction:
•  
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  6.432 Stochastic Processes, Detection, and Estimation
  
  
  
  

  Spring 2004

  
  
  

  
  
  

  Course Highlights

  
  
  

  This course site features homework assignments and recitation notes.

  
  
  

  Course Description

  
  
  

  This course examines the fundamentals of detection and estimation for signal processing, communications, and control. Topics covered include: vector spaces of random variables; Bayesian and Neyman-Pearson hypothesis testing; Bayesian and nonrandom parameter estimation; minimum-variance unbiased estimators and the Cramer-Rao bounds; representations for stochastic processes, shaping and whitening filters, and Karhunen-Loeve expansions; and detection and estimation from waveform observations. Advanced topics include: linear prediction and spectral estimation, and Wiener and Kalman filters.