Hierarchical model

Hello everyone,
I am new to pymc3 and I’d like to model some time series data with a hierarchical model.
Basically, I used pymc3 to derive parameters of a sum of exponential functions that describe my observed data over time. This part is working correctly if i provide one data set at a time.

with pm.Model() as model_biexpo:
    ke = pm.Exponential('ke', lam=1)
    ka = pm.Exponential('ka', lam=50)
    c = pm.Exponential('c', lam=1e-4)
    epsilon = pm.HalfNormal('epsilon',sigma=5)
    measures = pm.Deterministic('measures', bi_exponential(ke, ka, c, t))
    y_like = pm.Normal('y_like', mu=measures, observed=observed, sd=epsilon)
    dec_trace = pm.sample(15000, progressbar=False, tune=8000)
    burned_dec_trace = dec_trace[8000:]

observed & t are measured data and time of measurement respectively. So far so good. results are great.

Following this document & this one, I try to implement a hierarchical model to fit several dataset at the same time.

For my first try, i don’t want to look after hyperparameters in my model, I want to stick to the unpooled model like in radon example.

My data (as a pandas Dataframe) are arranged like so:

hours measures
0 3.95 6.044405e-06
0 22.25 2.673205e-06
0 46.07 1.961030e-06
0 70.81 1.262165e-06
0 142.49 7.512900e-07
1 2.00 1.355847e-05
1 26.04 1.151494e-06
1 49.77 8.385432e-07
2 to continue with some other data...

For each code, several time and measurements. Note that the number of time points are not necessary the same.

I don’t how to organize my data to fit into the model derived from here:

with pm.Model() as unpooled_model_biexpo:
    ke = pm.Exponential('ke', lam=1, shape=nb_sbjct)
    ka = pm.Exponential('ka', lam=50, shape=nb_sbjct)
    c = pm.Exponential('c', lam=1e-4, shape=nb_sbjct)
    t = theano.tensor.dcol('t')
    epsilon = pm.HalfNormal('epsilon',sigma=5)
    tps_measures =data.loc[:,'hours'].values.reshape(len(idx_pat),1).astype('double')
    measures = pm.Deterministic('measures', tt.function([ke, ka, c, t],
                                            bi_exponential(ke, ka, c, tps_measures), on_unused_input='warn'))
    y_like = pm.Normal('y_like', mu=measures, observed=data.loc[:,'measures'], sd=epsilon)
    dec_trace = pm.sample(15000, progressbar=False, tune=8000)
    burned_dec_trace = dec_trace[8000:]

if I understand correctly nb_subjct should be the number of different code in my dataset, say: nb_suject=len(data.index.unique()).

I don’t figure out how to handle the measurements properly as the number of data point vary from subject to subject. Despite my several attempts, I didn’t find a way to properly encode this information for each subject.

Any help would be greatly appreciated.
Best regards