Some models - such as GAS models - have likelihoods that are very sensitive to poor parameterization. This results in nan likelihoods, and thereby nan gradients for BBVI. Possible solutions include gradient clipping for GAS models, and a warm-up stage for BBVI that is specific to some model types.
This issue is mostly resolved - in the sense that we now give the user the chance to tweak the parameters for BBVI. But more documentation needed on BBVI tuning perhaps.
Some models - such as GAS models - have likelihoods that are very sensitive to poor parameterization. This results in nan likelihoods, and thereby nan gradients for BBVI. Possible solutions include gradient clipping for GAS models, and a warm-up stage for BBVI that is specific to some model types.