[Question] What is the difference between "model(test_x)" and "likelihood(model(test_x))" for prediction?

Hi, I am trying regression with GPyTorch following the example in the documentation.

In the example, there are two ways to get mean and variance of prediction.

f_preds = model(test_x)

y_preds = likelihood(model(test_x))

I noticed that the mean of prediction is the same when I use model(test_x) and likelihood(model(test_x)), but variance is different. I didn’t understand the reason. More generally, I didn’t understand the difference between model(test_x) and likelihood(model(test_x)).

Could you please guide me on this issue?

[jacobrgardner]

In the regression setting, the former does not include the likelihood noise (it is the latent variance) and the latter does include it.

[varunagrawal]

@gpleiss I was gonna do it even if you hadn’t asked. I may need a couple of days though, lots of things need finishing up currently.