This is taken from Statistical Rethinking Chapter 6

```
import pandas as pd
import torch
import torch.tensor as tensor
import pyro
import pyro.distributions as dist
from pyro.infer import SVI, Trace_ELBO
from pyro.infer.autoguide import AutoDiagonalNormal
sppnames = ["afarensis", "africanus", "habilis", "boisei", "rudolfensis", "ergaster", "sapiens"]
brainvolcc = [438., 452., 612., 521., 752., 871., 1350.]
masskg = [37.0, 35.5, 34.5, 41.5, 55.5, 61.0, 53.5]
d = pd.DataFrame({"species": sppnames, "brain":brainvolcc, "mass": masskg})
brain = tensor(d['brain'], dtype=torch.float)
mass = tensor(d['mass'], dtype=torch.float)
def modelLM(x, y):
sigma = 1.
weight = pyro.sample("weight", dist.Normal(0., 1.))
y_pred = weight * x
bias = pyro.sample("bias", dist.Normal(x.mean(), 100.))
y_pred = y_pred + bias
y = pyro.sample("y", dist.Normal(y_pred, sigma), obs=y)
return y
adam = pyro.optim.Adam({"lr": 0.03})
guide = AutoDiagonalNormal(modelLM)
svi = SVI(modelLM, guide, adam, loss=Trace_ELBO())
num_iterations = 5000
pyro.clear_param_store()
losses = []
for j in range(num_iterations):
# calculate the loss and take a gradient step
loss = svi.step(mass, brain)
if j % 100 == 0:
print("[iteration %04d] loss: %.4f" % (j + 1, loss / len(brain)))
losses.append(loss)
print(guide.quantiles([0.25, 0.5, 0.75]))
```

results in

```
{'bias': [tensor(20.3188), tensor(20.4905), tensor(20.6623)],
'weight': [tensor(15.4664), tensor(15.4902), tensor(15.5140)]}
```

while the expected bias is:

```
bias ~ -227.6287
weight ~ 20.6889
```

The losses are as high as 89155, which suggests there is something wrong in the way I am either definiing the loss or the choice of guide function. Any pointers?