text: Title: DOC Sample from prior predictive
  Body: From reading the [docs](http://num.pyro.ai/en/latest/tutorials/bayesian_regression.html), I can see that if I want to regress `y` onto `t`, I can do:
  ```python
  def model(t, y=None):
      a = numpyro.sample('a', dist.Normal(0., 0.5))
      b = numpyro.sample('b', dist.Normal(0., 0.5))
      sigma = numpyro.sample('sigma', dist.Exponential(1.))
      mu = a + b*t
      numpyro.sample('obs', dist.Normal(mu, sigma), obs=y)
  
  rng_key = random.PRNGKey(0)
  rng_key, rng_key_ = random.split(rng_key)
  
  # Run NUTS.
  kernel = NUTS(model)
  mcmc = MCMC(kernel, num_warmup=1000, num_samples=1000, num_chains=4)
  mcmc.run(rng_key_, t=t, y=y)
  mcmc.print_summary()
  samples_1 = mcmc.get_samples()
  ```
  
  I can then sample from the posterior predictive distribution with:
  ```python
  rng_key, rng_key_ = random.split(rng_key)
  predictive = Predictive(model, samples_1)
  predictions = predictive(rng_key_, t=t)['obs']
  ```
  
  However, how can I sample from the _prior_ predictive distribution?
  
  (I'm happy to make a PR to clarify this in the docs once I've understood it)