Hi everyone,

I am trying to learn Pyro to use in my modeling work and am currently following the introductory documentation.

I am trying to understand inference following the steps here:

Inference in Pyro: From Stochastic Functions to Marginal Distributions

I built this simple model of linear dependence + noise:

```
import numpy as np
import matplotlib.pyplot as plt
import torch
import pyro
import pyro.infer
import pyro.optim
import pyro.distributions as dist
torch.manual_seed(101);
def linear(x):
noise = pyro.sample('noise', dist.Normal(0, 0.5))
return 2.0 * x + noise
```

And this guide:

```
def linear_guide(x):
a = pyro.param("a", torch.tensor(3.0, requires_grad=True))
b = pyro.param("b", torch.tensor(1.0, requires_grad=True))
return a * x + pyro.sample('noise', dist.Normal(0, b))
```

Here is the inference code:

```
pyro.clear_param_store()
svi = pyro.infer.SVI(model=linear,
guide=linear_guide,
optim=pyro.optim.SGD({"lr": 0.001}),
loss=pyro.infer.Trace_ELBO())
losses = []
xs = np.random.choice(np.arange(100), 10000)
for x in xs:
losses.append((svi.step(float(x))))
plt.plot(losses)
plt.title("ELBO")
plt.xlabel("step")
plt.ylabel("loss")
```

Only one of the parameters seem to be converging:

```
pyro.param('b').item()
#0.529, real value 0.5
pyro.param('a').item()
#3.0, real value 2.0
```

I would appreciate any guidance as to what I am missing here.

Thanks in advance!

Yosi