decision_theory naragi

decision_theory naragi - EE7615 A Review of Decision and...

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EE7615 A Review of Decision and Estimation Theory If M is the outcome of some random experiment we would like to know but can not observe, and if R is the outcome of some random experiment we can observe, and if M and R are not independent, then it is reasonable to expect that R might profitably be used to make an estimate of the unobserved value of M . The study of rules for doing just this is the topic of decision and estimation theory. This theory tells us how to find a decision (or estimation) rule which, for any R = r that might occur, indicates a good guess for M . That is, an estimation rule is a function which assigns an estimate ˆ m to every potential observed value of R = r . In particular the theory can provide us with the best such rule. In order to characterize the best rule we must know the relationship between M and R , i.e., their joint distribution. In addition we must specify how to measure the ”goodness” of an estimation rule. Indeed, for different measures of goodness different rules turn out to be the best. Suppose we have two random variables (r.v.) M and R . M is discrete with alphabet Ω M = { m 1 ,m 2 ,...,m q } , while R can be discrete or continuous and has alphabet Ω R . The joint distribution of M and R is given by p MR ( m,r ). If R is discrete then p MR ( m,r ) is the probability mass function (pmf), while if R is continuous, p MR ( m,r ) is mixed. We want to know the value of M . But we can only observe the outcome of the r.v. R and must estimate
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This note was uploaded on 03/05/2012 for the course EE 7615 taught by Professor Naragipour during the Fall '11 term at LSU.

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decision_theory naragi - EE7615 A Review of Decision and...

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