Hello,

I am using PYMC to build an fit some GP models to some datasets. I’ve been able to fit conditional GP models through sampling and `find_MAP()`

; however, when drawing from the posterior, I can’t seem to control the number of posterior samples that are drawn. In the older PYMC GP tutorials a `samples`

argument could be passed to `sample_posterior_predictive()`

to control this value, but that has been removed in more recent versions it seems. If I use sampling to fit the covariance function parameters, the the posterior sample count is the same as the MCMC sample count. However, if I use `find_MAP()`

, I only draw one sample. How can I directly control posterior sample counts?

I’ve included my mode construction below:

```
with pm.Model() as model:
l = pm.Gamma("l", alpha=2, beta=1)
eta = pm.HalfCauchy("eta", beta=5)
M = pm.gp.mean.Constant(c=1)
K = eta**2 * pm.gp.cov.Matern52(1, l)
sigma = pm.HalfCauchy("sigma", beta=5)
gp = pm.gp.Marginal(mean_func=M, cov_func=K)
gp.marginal_likelihood('obs', X=X, y=y, sigma=sigma)
# fit = pm.find_MAP()
gp_trace = pm.sample(500, tune=500, chains=2)
f_pred = gp.conditional("f_pred", X_pred)
# gp_samples = pm.sample_posterior_predictive([fit], var_names=["f_pred"])
gp_samples = pm.sample_posterior_predictive(gp_trace, var_names=["f_pred"])
```

Thanks in advance!