Sure, the effect is additive (it is an initial approach in order to figure out how to handle shaping and indexing). But what is the shape of meanRunner? I have 108 runner so the first dimension will be 108, but not all of them have a race mean for every year because they haven’t run in every year. The years are 7.
If i write something like
meanRunner = pm.StudentT(“meanRunner”, nu=3, mu=meanYear,sigma=240,shape=(108,7))
I don’t know how the ‘empty’ means interfere with the inference.
So i was looping over the runners and i was creating 108 different variables with proper size for each runner.
e.g.
meanRunner1 = pm.StudentT(“meanRunner1”, nu=3, mu=meanYear,sigma=240,shape=3)
if runner1 had races in 3 years
meanRunner2 = pm.StudentT(“meanRunner2”, nu=3, mu=meanYear,sigma=240,shape=5)
if runner2 had races in 5 years.
The sampling was very slow but the results were good. So i am asking how i can handle such a situation in an effective way. I don’t have experience in working with tensors.