ECSE-2500, Engineering Probability, Spring 2010, Rensselaer Polytechnic Institute

# Lecture 24

# Grades to date

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# Chapter 6 ctd

- Example 6.9 p310 - cdf of max or min of n random variables
- Example 6.10
- Example 6.11

# Section 6.3 Expected values

- mean vector, correlation matrix, covariance matrix
- Example 6.16.

# Section 6.4 joint gaussian r.v.

# Section 6.5 Estimation of random variables

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

# Section 6.5.2 Minimum mean square error estimator

- Error has a cost. Minimize that.