I’m working with a model that is specifying a set of GPs, one for each group member in the dataset:

```
ls = pyro.sample('ls', Gamma(torch.FloatTensor([5.]).to(device), torch.FloatTensor([0.5]).to(device)))
amp = pyro.sample('amp', Gamma(torch.FloatTensor([2.]).to(device), torch.FloatTensor([1.]).to(device)))
with pyro.plate('venues', V):
K_w =gp.kernels.RBF(
input_dim=1,
variance=amp,
lengthscale=ls
)
cov_alpha = K_w(torch.FloatTensor(weeks).to(device))
cov_alpha.view(-1)[::W+1] += jitter
alpha = pyro.sample('alpha', MultivariateNormal(torch.zeros(W).to(device), covariance_matrix=cov_alpha))
```

This works fine, as you would expect. However, if I want to specify individual-specific length scales, I assume I just have to move the `ls`

statement into the plate:

```
amp = pyro.sample('amp', Gamma(torch.FloatTensor([2.]).to(device), torch.FloatTensor([1.]).to(device)))
with pyro.plate('venues', V):
ls = pyro.sample('ls', Gamma(torch.FloatTensor([5.]).to(device), torch.FloatTensor([0.5]).to(device)))
K_w =gp.kernels.RBF(
input_dim=1,
variance=amp,
lengthscale=ls
)
cov_alpha = K_w(torch.FloatTensor(weeks).to(device))
cov_alpha.view(-1)[::W+1] += jitter
alpha = pyro.sample('alpha', MultivariateNormal(torch.zeros(W).to(device), covariance_matrix=cov_alpha))
```

This model runs fine, but when I try to pull out estimates,

```
estimates = guide.quantiles([0.025, 0.5, 0.975])
```

I get a RuntimeError:

```
~/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/torch/distributions/normal.py in icdf(self, value)
83
84 def icdf(self, value):
---> 85 return self.loc + self.scale * torch.erfinv(2 * value - 1) * math.sqrt(2)
86
87 def entropy(self):
RuntimeError: The size of tensor a (441) must match the size of tensor b (3) at non-singleton dimension 0
```

Moreover, if I try and bring *both* hyperparameters into the plate, the model does not run at all:

```
with pyro.plate('venues', V):
ls = pyro.sample('ls', Gamma(torch.FloatTensor([5.]).to(device), torch.FloatTensor([0.5]).to(device)))
amp = pyro.sample('amp', Gamma(torch.FloatTensor([2.]).to(device), torch.FloatTensor([1.]).to(device)))
K_w =gp.kernels.RBF(
input_dim=1,
variance=amp,
lengthscale=ls
)
cov_alpha = K_w(torch.FloatTensor(weeks).to(device))
cov_alpha.view(-1)[::W+1] += jitter
alpha = pyro.sample('alpha', MultivariateNormal(torch.zeros(W).to(device), covariance_matrix=cov_alpha))
```

```
~/anaconda3/envs/pytorch_latest_p37/lib/python3.7/site-packages/pyro/contrib/gp/kernels/isotropic.py in forward(self, X, Z, diag)
87 r2 = self._square_scaled_dist(X, Z)
---> 88 return self.variance * torch.exp(-0.5 * r2)
89
RuntimeError: The size of tensor a (32) must match the size of tensor b (67) at non-singleton dimension 1
```

I get the feeling I’m just doing something dumb here, but I can’t see it. Is it something specific with GP parameters that is different?