Bayesian Linear Regression - CEDAR

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Machine Learning ! ! ! ! !SrihariPrior Distribution for ParametersAssume multivariate Gaussian prior for w (whichhas components w 0 ,..,w M-1 )p(w) = N (w|m 0 , S 0 )with mean m 0 and covariance matrix S 0If we choose S 0 = α -1 I it means that the variances of theweights are all equal to α -1 and covariances are zerop(w) with zero mean (m 0 =0)and isotropic over weights (same variances)w 1 w 0 5
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Bayesian Linear Regression - CEDAR

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