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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:
  •  


  • 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.
     

ACKNOWLEDGEMENT:
This course content is a redistribution of MIT Open Courses. Access to the course materials is free to all users.






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