Bayesian Estimation: Chapters 10-12 by Natasha Devroye, Study notes of Electrical and Electronics Engineering

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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Estimation: parts of Chapters 10-12
Bayesian Estimation
Natasha Devroye
http://www.ece.uic.edu/~devroye
Spring 2010
Summary
Bayesian philosophy
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Estimation: parts of Chapters 10-

Bayesian Estimation

Natasha Devroye

[email protected]

http://www.ece.uic.edu/~devroye

Spring 2010

Summary

Bayesian philosophy

Bayesian risk

Bayesian risk

Hit-or-miss cost (MAP)

MMSE estimators

MMSE estimators

Example of MMSE

Bayesian linear model

Bayesian linear model

Examples: MMSE in Gaussian noise

Examples: MMSE in Gaussian noise

Maximum a Posteriori (MAP) estimation

Example

MAP properties

Linear MMSE (LMMSE)

Geometric LMMSE

LMMSE example

LMMSE properties

LMMSE properties

Sequential LMMSE: geometric approach

3

“Hit-or-Miss”

Cost Function

1! 1 !

ˆ |

Err.Cov.:

Estimate:

“Squared” Cost Function

(Nonlinear Estimate)

7,+-$ Linear Estimate

:',;' : E{$},E{ 1 }, C

Jointly Gaussian 1 and $

(Yields Linear Estimate)

!!! 1 11 1! !

! 1 11

1

ˆ

1

Err.Cov. :

Estimate:

'

'

!!! 1 11 1! !

! 1 11

1

ˆ

1

Err.Cov. :

'

'

Estimate #! ( '!

Bayesian Linear Model

(Yields Linear Estimate)

!!!! !

!!!

1

ˆ

1

Err.Cov. :

Estimate:

'

'

"

# #

"

# #

!

See http://www.ws.binghamton.edu/fowler/fowler%20personal%20page/EE522_files/EECE%20522%20Notes_28%20Ch_12B.pdf