I have the following to model my univariate time series data:

```
with pm.Model() as model:
ℓ = pm.Gamma("ℓ", alpha=2, beta=1)
η = pm.HalfCauchy("η", beta=5)
cov = η**2 * pm.gp.cov.Matern52(1, ℓ)
gp = pm.gp.Latent(cov_func=cov)
f = gp.prior("f", X = train_x)
σ = pm.HalfCauchy("σ", beta=5)
ν = pm.Gamma("ν", alpha=2, beta=0.1)
y_ = pm.StudentT("y", mu=f, lam=1.0/σ, nu=ν, observed=train_y)
trace = pm.sample(50, tune=10, cores=4)
```

However, when I sample from the posterior by using the block below, the shape I get for pred_samples[‘y’] is `(50, 48, 48)`

:

```
with model:
f_pred = gp.conditional("f_pred", train_x)
# Sample from the GP conditional distribution
pred_samples = pm.sample_posterior_predictive(trace, vars=[f_pred, y_], samples=50)
```

So I don’t quite understand why each sample is 48 x 48. Shouldn’t it be simple 50x48 (which is the shape of `pred_samples['f_pred']`

). I have included a simple test script here in this colab notebook which runs the above.

Any thoughts/ comments would be highly appreciated.