Hey! I’m trying to implement a multidimensional sparse GP but I’m unsure about the syntax for passing arguments to the marginal likelihood function. I’ve managed the following:

` with pm.Model() as model:

```
ℓ = pm.HalfNormal('ℓ', sigma=5)
lenght_scale_x = pm.HalfNormal('lenght_scale x', sigma=5)
lenght_scale_c = pm.HalfNormal('lenght_scale c', sigma=5)
η = pm.HalfCauchy("η", beta=5)
cov = η**2 * \
pm.gp.cov.Exponential(1, ls=lenght_scale_c) * \
pm.gp.cov.Periodic(1, period=ℓ, ls=lenght_scale_x)
σ = pm.HalfNormal("σ", sigma=0.1)
gp = pm.gp.MarginalSparse(cov_func = cov, approx="FITC")
# Inducing points for the sparse aproximation
Xu = pm.Uniform("Xu",lower = 0, upper = X_1.max(),
shape=(10,))
C_u = pm.Uniform("C_u",lower = 1e-8, upper = c.max(),
shape=(10,))
input_u = tt.concatenate([Xu, C_u], axis = 1)
y_ = gp.marginal_likelihood("y", X=input_1, Xu=input_u, y=f_true, noise=σ)`
```

Which gives the following error:

`ValueError: Join argument "axis" is out of range (given input dimensions)`

I’ve tried transposing the arrays entering input_u and transposing input_u but it doesn’t seem to work that way.

Also, is the cov function correctly defined? I started this using a kronecker covariance and thought it would be usefull to define the cov func this way when trying to implement the sparse approximation.

Any help would be highly appreciated!