so ive been following this tutorial:

https://pyro.ai/examples/bayesian_regression_ii.html

but I got stuck toward the end. We draw 1000 samples and come up with a posterior distribution using MCMC, however, how do I go abut making predictions from this point?

i found this discussion:

Dear fellow pyromants,
i have a question regarding how to obtain the posterior predictive distribution using MCMC techniques.
Here is the little example code i am trying to run:
# generate some data
np.random.seed(1)
xs = to.tensor(np.linspace(-2,2,23)).float()
f = lambda z : z*.5 + 1
ys = f(xs) + to.tensor(np.random.normal(0, .5, size=len(xs))).float()
def y_model(x, y_obs=None):
''' very simple linreg model'''
# priors
w = pyro.sample('w', pyro.distributions.Normal(loc=0, scaleā¦
but it seems to be discontinued.

please help me! ty