Hi,

I would like to fit only the bias of a multilevel model (**level 1** with **20 categories** and **level 2** with **350**) over time (**15 periods**)

To do that I did the following.

```
import theano.tensor as tt
import pymc3 as pm
model_randomwalk = pm.Model()
with model_randomwalk:
# std of random walk
sigma_bias_level_1 = pm.Exponential('sigma_alpha', 50., shape=20)
cov_alpha = tt.diag(sigma_bias_level_1)
sigma_bias_level_2 = pm.Exponential('sigma_beta', 50., shape=350)
cov_beta = tt.diag(sigma_bias_level_2)
bias_level_1 = pm.MvGaussianRandomWalk('alpha', cov=cov_alpha, shape=(15, 20)
bias_level_2 = pm.MvGaussianRandomWalk('beta', cov=cov_beta, shape=(15, 350)
with model_randomwalk:
regression = bias_level_1[index_ts, index_level_1] + bias_level_2[index_ts, index_level_2]
sd = pm.HalfNormal('sd', sd=.1)
likelihood = pm.Normal('y',
mu=regression,
sd=sd,
observed=y)
with model_randomwalk:
trace_rw = pm.sample(tune=500, cores=2, init='auto')
```

But the sample method gets stuck at the beginning using *jitter+adapt_diag*. Is there something I could do better ?

Thank you