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131A_1_131A_1_Lecture-Outline

# 131A_1_131A_1_Lecture-Outline - 7 10 11 12 13 14 15 16 17...

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Unformatted text preview: 7. 10. 11. 12. 13. 14. 15. 16. 17. Lecture 1: Lecture 2: Lecture 3: Lectures 4 and 5: Lecture 6: Lecture 6: Lectures 7 and 8: Lecture 9: Lecture 10: Lecture 11: Lecture 12: Lecture 13: Lecture 14: Lecture 15: Lecture 16: Lecture 17: Lecture 18: Expanded Course Outline for EE 131A Introductory examples and overview Axioms of probability / Basic deﬁnitions Conditional probability / Bayes’ rule / Independence of events Probabilities via counting methods Binomial, multinomial and geometric probability laws / Dependent experiments Types of random variables / Cumulative distribution function (cdf) Probability density function (pdf) / Conditional cdf and pdf Functions of a random variable / Binomial, Geometric and Poisson random variables Gaussian and Exponential random variables / General functions of a random variable Expected value and Variance of a random variable / Markov and Chebyshev inequalities Fitting data to a distribution / Transform methods (characteristic function, probability generating function, Laplace transform of a pdf) Pairs of random variables / Joint and marginal cdf and pdf Independence of random variables / Conditional probability Conditional expectation / Multiple random variables (joint, marginal and conditional cdf and pdf) Functions of several random variables / Transformations of random vectors / pdf of linear and general transformations Expected value of functions of random variables / Correlation and covariance Sums of random variables / Sample mean ...
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