Question: In probabilistic PCA, the observations are generated from latent variable z with add noise epsilon: = WztuTEIt can be shown that p(z|x) = N(z/M-1WT (x

In probabilistic PCA, the observations are generated from latent variable z with add noise epsilon:

add noise epsilon: = WztuTEIt can be shown that p(z|x) = N(z/M-1WT

= WztuTEIt can be shown that p(z|x) = N(z/M-1WT (x - M), M-10-2) where M is defined as M = WTW+021Show that as a > 0, the posterior mean for 2 given x becomes E(Z|m) = (WTW)_1WT($ - u)

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