Transcription of Chapter 13 The Multivariate Gaussian - EECS at UC Berkeley
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Chapter 13 The Multivariate Gaussian - EECS at UC Berkeley
Chapter 13 The Multivariate GaussianIn this Chapter we present some basic facts regarding the Multivariate Gaussian discuss the two major parameterizations of the Multivariate Gaussian themomentparameterizationand thecanonical parameterization, and we show how the basic operationsof marginalization and conditioning are carried out in thesetwo parameterizations. We alsodiscuss maximum likelihood estimation for the Multivariate ParameterizationsThe Multivariate Gaussian distribution is commonly expressed interms of the parameters and , where is ann 1 vector and is ann n, symmetric matrix. (We will assumefor now that is also positive definite, but later on we will haveoccasion to relax thatconstraint).
Chapter 13 The Multivariate Gaussian ... we will see, different kinds of transformations are more readily carried out in one represen-tation or the other. 13.2. JOINT DISTRIBUTIONS 3 13.2 Joint distributions Suppose that we partition the n×1 vector x …
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