Hello,

I’m having trouble understanding what’s going when pm.sample_ppc is called. I made some data generated by a simple linear regression and then drew samples from the posterior predictive distribution with this code:

```
with pm.Model() as model1:
x = pm.Uniform('x',lower = 0.0, upper = 10.0, observed = observed_x,shape = n)
sigma = pm.HalfCauchy('sigma',beta = 1.0)
beta = pm.Normal('beta',sd = 5.0)
alpha = pm.Normal('alpha',sd = 5.0)
y_hat = beta * x + alpha
y = pm.Normal('y',mu = y_hat,sd = sigma, shape = n,observed = observed_y)
trace = pm.sample()
ppc = pm.sample_ppc(trace, samples=1, model=model1)
```

Then, I plot the samples of Y versus the samples of X

`plt.scatter(ppc['x'],ppc['y'])`

and I find that they are uncorrelated:

Now, I was expecting it to instead look like this:

`plt.scatter(observed_x,ppc['y'])`

In short, my question is this: why are the sampled X and Y values not consistent with regard to the model logic?