In this lecture, we discuss expectation of a discrete random variable as a representation of random numbers followed by expected value rule and variance-covariance.
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L01 Introduction to probability
L46 Estimator of a random vector
L02 Basic concepts of probability
L03 Probabilistic modelling
L06 Applications of conditional probability
L05 Conditional probability
MA 451 L25 First order optimality condition
L04 Algorithm for probabilistic modelling
Matrix decompositions and dimensionality reduction
L16 Conditional probability mass function
L34 Stochastic convergence