I am trying to add a covariance matrix inside a Gaussian Processes using pymc3, this is my code:

```
import matplotlib.pyplot as plt
import numpy as np
import pymc3 as pm
%matplotlib inline
#Data
X = np.array([1,2,3,4])
Y= np.array([1,4,9,16])
Sigma = np.matrix([[1,0.1, 0,0], [0.1, 2,0.2,0.4], [0, 0.2,3,0.4],[0, 0.4,0.4,4]]) #Covariance Matrix
le= len(X)
#Reorder the data stored in X
Xg = X.reshape((le,1))
with pm.Model() as model:
ℓ = pm.Uniform("ℓ", lower=0,upper=2.5)
η = pm.Uniform("η", lower=0,upper=0.5)
cov = η ** 2 * pm.gp.cov.Matern52(1, ℓ)
gp = pm.gp.Marginal(cov_func=cov)
y_ = gp.marginal_likelihood("y", X=Xg, y=Y, noise=Sigma)
mp = pm.find_MAP()
```

However I get the following error:

```
TypeError Traceback (most recent call last)
<ipython-input-69-4911417cefce> in <module>
---> 13 y_ = gp.marginal_likelihood("y", X=Xg, y=Y, noise=Sigma)
~Anaconda3envspmflibsite-packagespymc3gpgp.py in marginal_likelihood(self, name, X, y, noise, is_observed, **kwargs)
441 if not isinstance(noise, Covariance):
442 noise = pm.gp.cov.WhiteNoise(noise)
--> 443 mu, cov = self._build_marginal_likelihood(X, noise)
444 self.X = X
445 self.y = y
~Anaconda3envspmflibsite-packagestheanotensorbasic.py in make_node(self, value, *shape)
2991 " than the specified dimensions",
2992 v.ndim,
-> 2993 len(sh),
2994 )
2995 otype = TensorType(dtype=v.dtype, broadcastable=bcast)
TypeError: ('The Alloc value to use has more dimensions than the specified dimensions', 2, 1)
```

The only way I can avoid the error is by placing a diagonal matrix for Sigma, for example:

```
Sigma = [1,2,3,4]
```

Any suggestions?

Source: Python-3x Questions