I have a GP that I am fitting in Pyro where I am setting the lengthscale to a large constant, based on prior knowledge:

```
amp = pyro.sample('amp', Gamma(torch.DoubleTensor([2.]).to(device),
torch.DoubleTensor([0.5]).to(device)))
K = gp.kernels.RBF(
input_dim=1,
variance=amp,
lengthscale=torch.tensor(100.).to(device)
)
cov_beta = K(torch.DoubleTensor(days).to(device))
cov_beta.view(-1)[::D+1] += jitter
beta = pyro.sample('beta', MultivariateNormal((torch.ones(D).to(device)),
covariance_matrix=cov_beta))
```

however, when I fit this model, a plot of beta looks as follows:

clearly the lengthscale is much smaller than 100 here. Does SVI change the values of the lengthscale, even though I have specified it, with no prior? It seems like I am doing something wrong here.

I even tried putting a uniform prior on the lengthscale with a lower bound of 100, and I get a similar result:

```
ls = pyro.sample('ls', Uniform(torch.DoubleTensor([100.]).to(device), torch.DoubleTensor([200]).to(device)))
```

Any ideas?