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An overview of bayesian estimation, including the bayesian philosophy, risk, and various cost functions. Mmse estimators, maximum a posteriori (map) estimation, and linear mmse. It also includes examples and properties of mmse and lmmse. Crucial for students studying bayesian estimation, particularly in the context of gaussian priors and noise.
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3
“Hit-or-Miss”
Cost Function
1! 1 !
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Err.Cov.:
Estimate:
“Squared” Cost Function
(Nonlinear Estimate)
7,+-$ Linear Estimate
Jointly Gaussian 1 and $
(Yields Linear Estimate)
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1
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1
Err.Cov. :
Estimate:
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1
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1
Err.Cov. :
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Estimate #! ( '!
Bayesian Linear Model
(Yields Linear Estimate)
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1
Err.Cov. :
Estimate:
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