2009. 5. 27. 21:01
@GSMC/홍대형: Statistical Communication Theory
Yates & Goodman
Probability and Stochastic Process, 2nd ed.
Chapter 9 Estimation of a Random Variable
prediction
A predictor uses random variables produced in early subexperiments to estimate a random variable produced by a future subexperiment.
9.1 Optimum Estimation Given Another Random Variable
Bind estimation of X
Estimation of X given an event
Minimum Mean Square Estimation of X given Y
9.2 Linear Estimation of X given Y
9.3 MAP and ML Estimation
9.4 Linear Estimation of Random Varaiables from Random Vectors
Probability and Stochastic Process, 2nd ed.
Chapter 9 Estimation of a Random Variable
prediction
A predictor uses random variables produced in early subexperiments to estimate a random variable produced by a future subexperiment.
9.1 Optimum Estimation Given Another Random Variable
The estimate of X that produces the minimum mean square error is the expected value (or conditional expected value) of X calculated with the probability model that incorporates the available information.
Bind estimation of X
Estimation of X given an event
Minimum Mean Square Estimation of X given Y
9.2 Linear Estimation of X given Y
9.3 MAP and ML Estimation
9.4 Linear Estimation of Random Varaiables from Random Vectors
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