Hello,

I have a question regarding MC dropout. I understand that we can estimate uncertainty by taking repeated forward passes at test time with dropout enabled and grad turned off.

My question is, what if we wanted to use a loss function that considers uncertainty… How do we ensure that the uncertainty calculated at test time is optimized in the loss function?

Thank you

Here’s how I have calculated the uncertainty

```
with torch.no_grad():
for _ in range(100):
mc_y_pred = model(X_train)
mc_predictions.append(mc_y_pred)
mc_predictions = torch.stack(mc_predictions)
sigma = torch.std(mc_predictions, dim=0)
#Here y_pred could be the mean of mc_predictions
y_pred = model(X_train_batch)
loss = some_loss_func(y_true, y_pred, sigma)
##backwards pass
optimizer.zero_grad()
loss.backward()
#update weights
optimizer.step()
```