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Variational inference for latent variables and uncertain inputs in Gaussian processes

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Figure

Figure 1: A Gaussian distribution propagated through a non-linear mapping. yi = f(xi) +ǫi
Figure 2: A three dimensional manifold formed by mapping from a two dimensional spaceto a three dimensional space.
Figure 3: The graphical model for the GP-LVM (a) is augmented with auxiliary variables to obtainthe variational GP-LVM model (b) and its dynamical version (c)
Figure 4: Typical sample paths drawn from the kx(per) + kx(rbf) covariance function. Thevariances are fixed for the two terms, controlling their relative effect
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