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Chapter 6 ctd

1. Example 6.9 p310 - cdf of max or min of n random variables
2. Example 6.10
3. Example 6.11

Section 6.3 Expected values

1. mean vector, correlation matrix, covariance matrix
2. Example 6.16.

Section 6.4 joint gaussian r.v.

Section 6.5 Estimation of random variables

1. What's the best guess for an inaccessible random variable X, when we've observed an accessible r.v. Y?
2. MAP vs ML estimators
3. MAP requires that we know the prior probability.
4. Ex 6.25 (note: Ex 5.16 is on page 252.)
5. Ex 6.26.

Section 6.5.2 Minimum mean square error estimator

1. Error has a cost. Minimize that.