I have a multiple regression model. y is dependent with shape of
(12000,) and x is independent with shape of
my model is
pm.math.dot(x_shared,a)+c) where x_shared is
. After sampling (30000 samples), I want to obtain posterior predictive for x_new with shape of (400,23). I use x_shared.set_value(xx_new) and then use
posterior_pred = pm.sample_posterior_predictive(trace, model=model, samples=100). At the end of the day I expect
(100,400), amazingly I get
(100,12000)! Noted 12000 is my input data size for 23 independent variables.
I really confused why I do not get the correct shape for my predictive values? The only difference I can see with solved examples, is my input is a matrix, not a vector, because it is a multiple regression not a one dimensional regression.