I’m trying to implement a Gaussian Process regressor using Edward

I’ve used the following code to build the model

```
X = tf.placeholder(tf.float32, [N, 1])
f = MultivariateNormalFullCovariance(
loc=tf.zeros([N]),
covariance_matrix=rbf(X)
)
y = MultivariateNormalDiag(
loc=f,
scale_diag=tf.ones([N]) * 0.3)
```

and then the proposal distribution and inference as follows

```
qf = MultivariateNormalFullCovariance(loc=tf.Variable(tf.random_normal([N])),
covariance_matrix=tf.Variable(tf.random_normal([N, N])))
inference = ed.KLqp({f: qf}, data={X: x_train, y: y_train})
inference.run(n_iter=5000)
```

I feel like I might have got something wrong. The inference throws an error saying that the Cholesky decomposition was unsuccessful.

Are there any examples that could help?