[RP]Lecture Note IX - Kyung Hee University Department of...

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Kyung Hee University Random Processing Department of Electronics and Radio Engineering Prof. Hyundong Shin Communications and Coding Theory Laboratory (CCTLAB) IX-1 C1002900 RP Lecture Handout IX: Bayesian Estimation Reading: Chapter 8.2 We want to estimate a vector x based on observation of a related vector Y . If the quantity to be estimated as a random vector X , then the conditional den- sity  f YX yx fully characterizes the relationship between X and Y . 9.1. Bayesian Formulation In the Bayesian framework, we refer to the density   f X x for the vector n X of the quantities to be estimated as the prior density. This density fully specifies our knowledge about X prior to any observation of the measurement Y . The conditional density f , which fully specifies the way in which Y contains information about X , is often not specified directly but is informed from a measure- ment model. Example 9.1: Suppose Y is a noise-corrupted measurement of some function of X   h  W . (IX.1) Then, ff h  W y x . (IX.2) We can also get the posterior density for X , i.e., the density for X given that Yy has been observed as
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This note was uploaded on 06/10/2010 for the course ELECTRONIC C1002900 taught by Professor Hyungdongshin during the Spring '10 term at Kyung Hee.

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[RP]Lecture Note IX - Kyung Hee University Department of...

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