Wallace Vander Velde

Wallace Vander Velde - 16.322 Stochastic Estimation and...

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16.322 Stochastic Estimation and Control, Fall 2004 Prof. Vander Velde Lecture 1 Pre-requisites 6.041 – Probabilistic systems Summary of the subject (topics) 1. Brief review of probability a. Example applications 2. Brief review of random variables a. Example applications 3. Brief review of random processes a. Classical description b. State space description 4. Wiener filtering 5. Optimum control system design 6. Estimation 7. Kalman filtering a. Discrete time b. Continuous time Textbook Brown, R.G. and P.Y.C. Hwang. Introduction to Random Signals and Applied Kalman Filtering. Course Administration All important material presented in class Read text and other references for perspective Do the suggested problems for practice – no credit is offered for these Two (2) hour-long quizzes will be held in-class – open book One (1) three hour final exam – open book Page 1 of 7
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16.322 Stochastic Estimation and Control, Fall 2004 Prof. Vander Velde Brief Introduction to Probability References: 1. Papoulis. Probability, Random Variables, and Stochastic Processes. (Best for probability and random variables) Define the model of uncertainty – the random experiment. Discrete sample space Continuous sample space Outcomes are points, spanning sample Outcomes are not points, but span a space continuum A probability function must be defined over the sample space of the random experiment.
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This note was uploaded on 11/07/2011 for the course AERO 16.322 taught by Professor Wallacevandervelde during the Fall '04 term at MIT.

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Wallace Vander Velde - 16.322 Stochastic Estimation and...

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