In this lecture, we discuss estimation of a random variable with the least mean squared error and linear hypothesis function followed by estimation of a random vector with least mean squared error and linear hypothesis function.
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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