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, scale=10))
b = pyro.sample('b', pyro.distributions.Normal(loc=0, scale=10))
f_x = w*x + b
y = pyro.sample('y', pyro.distributions.Normal(loc=f_x, scale=1), obs=y_obs)
return f_x
```

I can now sample models from the prior by just running

`samples_from_prior = y_model(xs, None)`

For inference, as described in the title, i want to use MCMC techniques:

```
from pyro.infer.mcmc import MCMC, NUTS
posterior_trace = MCMC(kernel=NUTS(y_model), num_samples=1337, warmup_steps=100)
posterior_trace.run(xs, ys)
```

The `posterior_trace`

is now an `pyro.infer.mcmc.mcmc`

object, on which i can call `posterior_trace.marginal(sites=..).empirical[..]`

to get the samples for the respective random variables “w” and “b” (and also ‘y’). And this is about how far the tutorial goes.

But now i would like to sample models `y_model`

given the parameter configurations in the `posterior_trace`

. I am not sure how this is ment to be done.

In case of SVI and a given guide i usually did the following:

`model_predictions = []`

`for _ in range(100): # sample 100 models (i.e. posteriors given y_obs)`

```
## trace of the guide (samples all variables given x from the guide)
guide_trace = pyro.poutine.trace(guide).get_trace(xs, None)
# runs the model using samples from the guide for the latent variables
replay_result = pyro.poutine.replay(y_model, guide_trace)(xs, None)
# append to list
model_predictions.append(replay_result.detach().numpy())
```

which would give me 100 sampled models from the (guide-approximated) posterior predictive distribution.

How can i achieve the same using the results obtained from MCMC ?

Thanks in advance !

PS:

- What tutorial are you running? (a very slim version of)

http://pyro.ai/examples/bayesian_regression_ii.html - What version of Pyro are you using?

‘0.3.1’