Hi I am having an issue imputing missing data in the following model:

```
class LinearMean(pm.gp.mean.Mean):
def __init__(self, intercept, slopes,M,G):
self.intercept = intercept
self.slopes = slopes
def __call__(self, X):
return self.intercept +self.slopes[0]*M +self.slopes[1]*G
with pm.Model() as fMBG_OU:
Mobs = pm.MutableData('Mobs',scale(np.log(primBnan['body'])))
Bobs = pm.MutableData('Bobs',scale(np.log(primBnan['brain'])).reshape(-1, 1))
Gobs = pm.MutableData('Gobs',scale(np.log(primBnan['group_size'])))
Dmat = pm.MutableData('Dmat',
(Dmat0[primBnan.index,primBnan.index]/np.max(Dmat0)).values)
rho = pm.Exponential('rho',.25)
etasq = pm.Exponential('etasq',0.25)
# SIGMA = pm.HalfNormal('SIGMA',0,1)
bM = pm.Normal('bM',0,0.5)
bG = pm.Normal('bG',0,0.5)
a = pm.Normal('a' ,0,1)
mu_G = pm.Normal('mu_G',0,1)
sigma_G = pm.Exponential('sigma_G',1)
G = pm.Normal('G',mu_G,sigma_G,observed=Gobs)
mu_M = pm.Normal('mu_M',0,1)
sigma_M = pm.Exponential('sigma_M',1)
M = pm.Normal('M',mu_M,sigma_M,observed=Mobs)
cov = etasq * pm.gp.cov.ExpQuad(input_dim=1,ls=rho)
mu = LinearMean(intercept = a,
slopes = [bM,bG],
M=M,
G=G)
gp = pm.gp.Marginal(mean_func=mu,cov_func=cov)
B = gp.marginal_likelihood('B',X=Dmat,y=Bobs,sigma=0)
fMBG_OU_trace = pm.sample()
```

Mobs and Gobs contain Nan values which I wish to impute.

If I individually model Mobs/Gobs. Like so:

```
with pm.Model() as Gimp:
mu = pm.Normal('mu',0,1)
sigma = pm.Exponential('sigma',1)
G = pm.Normal('G',mu,sigma,observed=scale(np.log(primBnan['group_size'])))
gimp_trace = pm.sample()
```

Imputation occurs and I get values for G_missing.

Also, If I fill all the Nan values with 1, the complete model samples correctly.

Where am I going wrong?