Hello team, I am trying to use SVI in numpyro to train a neural network. Since I do not know too much about the weights and biases of the neural network a-priori, I want SVI to focus more on the likelihood than on the prior. Is there a way to achieve that? I read somewhere that:

- I can increase the spread of the likelihood distribution
- I can use numpyro.factor to scale my likelihood by a factor which adjusts its weightage in the loss function.

Is any of this correct? At the moment, my data is not fitting well to the modelâ€™s predictions and I have a strong suspicion that the choice of prior may very well be the reason behind it. Thanks for the help.