I am using Bayesian linear regression y=beta*X+alpha for prediction. After inference, I tried to used 2 methods:
(1) y_post.eval(feed_dict={X_in: X4pre})
(2) I first evaluate the mean value of beta and alpha by beta_post.mean().eval() and alpha_post.mean().eval(), then use the equation y=beta*X+alpha to calculate the mean of y.

The predicted mean of y are quite close when I run the tutorial example: http://edwardlib.org/tutorials/supervised-regression

But when I tried to feed the model with some real noisy data from financial market, the predicted mean of y is quite different between these two methods. Is there any clue to solve this problem?