The VGP Opper-Archambeau variational parameters carry per-point meaning that could be exposed for diagnostics. alpha is the expected score (a representer weight and pseudo-residual showing each point's influence) and lambda is the expected per-point information (the effective precision). Their reading depends on the likelihood: under Poisson lambda is the expected rate, under Bernoulli it peaks at the decision boundary, and under Student-t it drops toward zero for discounted outliers. Adding a helper to extract alpha, lambda, and diag(S) from a fit aligned to the training inputs, with the per-likelihood interpretations documented and checked against the Opper and Archambeau (2009) paper, would be worth doing. A skill file describing how to read these parameters for fit and data diagnosis should be included as well.
The VGP Opper-Archambeau variational parameters carry per-point meaning that could be exposed for diagnostics.
alphais the expected score (a representer weight and pseudo-residual showing each point's influence) andlambdais the expected per-point information (the effective precision). Their reading depends on the likelihood: under Poissonlambdais the expected rate, under Bernoulli it peaks at the decision boundary, and under Student-t it drops toward zero for discounted outliers. Adding a helper to extractalpha,lambda, anddiag(S)from a fit aligned to the training inputs, with the per-likelihood interpretations documented and checked against the Opper and Archambeau (2009) paper, would be worth doing. A skill file describing how to read these parameters for fit and data diagnosis should be included as well.