Hi everyone! I’m starting exploring this package and I had some problems recreating the gaussian mixture example from bayesian methods for hackers (https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb).

This is my snippet

```
from jax import random
import jax.numpy as jnp
import numpyro
import numpyro.distributions as dist
from numpyro.infer import MCMC, NUTS
import pandas as pd
def model(data):
with numpyro.plate('samples', len(data)):
p_i = numpyro.sample('p_i', dist.Uniform(0, 1))
c_i = numpyro.sample('c_i', dist.Bernoulli(p_i))
mus = numpyro.sample('mus', dist.Normal(jnp.array([120, 190]), 10).to_event())
sds = numpyro.sample('sds', dist.Uniform(0, 100).expand([2]).to_event())
center_i = mus[c_i]
sd_i = sds[c_i]
with numpyro.plate('samples', len(data)):
obs = numpyro.sample('obs', dist.Normal(center_i, sd_i), obs=data)
data = pd.read_csv('https://raw.githubusercontent.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/master/Chapter3_MCMC/data/mixture_data.csv', header=None)
data = data.values.flatten()
rng_key = random.PRNGKey(42)
rng_key, rng_key_ = random.split(rng_key)
kernel = NUTS(model)
num_samples = 2000
mcmc = MCMC(kernel, num_warmup=1000, num_samples=num_samples)
mcmc.run(
rng_key_, data=data
)
```

The problem is that in the example linked above, the averages of the `p_i`

ranges from `0`

to `1`

as reported in the following plot

while in my implementation using

`numpyro`

they range only between about `0.35`

and `0.65`

:Is it related to some problem/bug with my implementation or could be some convergence issue?

Thanks!