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Engineering Probability Lecture 15: Joint expectations; correlation and covariance
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Classroom Contents
Engineering Probability Lectures, Fall 2018
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- 1 Engineering Probability Lecture 1: Experiments, Sample Spaces, and Events
- 2 Engineering Probability Lecture 2: Axioms of probability and counting methods
- 3 Engineering Probability Lecture 3: Conditional probability
- 4 Engineering Probability Lecture 4: Independent events and Bernoulli trials
- 5 Engineering Probability Lecture 5: Discrete random variables
- 6 Engineering Probability Lecture 6: Expected value and moments
- 7 Engineering Probability Lecture 7: Conditional probability mass functions
- 8 Engineering Probability Lecture 8: Cumulative distribution functions (CDFs)
- 9 Engineering Probability Lecture 9: Probability density functions and continuous random variables
- 10 Engineering Probability Lecture 10: The Gaussian random variable and Q function
- 11 Engineering Probability Lecture 11: Expected value for continuous random variables
- 12 Engineering Probability Lecture 12: Functions of a random variable; inequalities
- 13 Engineering Probability Lecture 13: Two random variables (discrete)
- 14 Engineering Probability Lecture 14: Two random variables (continuous); independence
- 15 Engineering Probability Lecture 15: Joint expectations; correlation and covariance
- 16 Engineering Probability Lecture 16: Conditional PDFs; Bayesian and maximum likelihood estimation
- 17 Engineering Probability Lecture 17: Conditional expectations
- 18 Engineering Probability Lecture 18: Sums of random variables and laws of large numbers
- 19 Engineering Probability Lecture 19: The Central Limit Theorem
- 20 Engineering Probability Lecture 20: MAP, ML, and MMSE estimation
- 21 Engineering Probability Lecture 21: Hypothesis testing
- 22 Engineering Probability Lecture 22: Testing the fit of a distribution; generating random samples
