moved from https://github.com/blei-lab/edward/issues/474.

@yyaodong

Hi Dustin:

Let’s say you want to build a predictive model in a Bayesian way, to predict

`y_i`

based on`x_i`

, and in the graph definition`P(y_i, x_i, z_i, beta),`

there is a local latent variable`z_i`

that is dependent on`x_i`

and global parameter`beta`

, and`y_i`

is dependent on all the rest.

I want to know, in this case, after making inference on

`z_i`

and`beta`

based on the training data, how to make predictions when the test data`x_test`

comes in. Since`z_i`

is dependent on`x_i`

, how can I update`z_i`

to the`z_test`

? As in my understanding, the approximator`q_z_i`

is only valid on the training data set, therefore, you cannot simply`q_z_i.copy()`

, and in GMM, the`z_i`

can just be easily reset by`qz_init.run()`

, because`z_i`

is not dependent on`x_i`

.

Many thanks,