VIBESVariational Inference for Bayesian Networks |
[Sourceforge project page] John Winn, January 2004 |
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3. Creating and learning a Gaussian modelAs the model stands, the node x has been marked as Gaussian by default and so the
model is invalid as neither the mean nor the precision of the
Gaussian have been set (attempting to initialise the model by
pressing the The model is still invalid as the parameters of μ and γ are unspecified. In this case, rather than create further latent variables, these parameters will be set to fixed values to give appropriate priors (for example setting μ to have mean=0 and precision=0.3 and γ to have a=10 and b=1). Note: If you want to skip constructing this network by hand, it is
in the tutorial file called The network now corresponds to a two-dimensional
Gaussian model and variational inference can be
performed automatically by pressing the |