I have my users' profiles which is based on the frequency of action in 10 bins.
1,2,3,4,5,6,7,8,9,10
User.ID[0,0,0,0,0,0,0,0,0,100]
[0,0,0,0,100,0,0,0,0,0]
...
n=609 [0,20,0,30,0,0,0,50,0,0]
I am trying to identify the percentage or number of users (out of 609) who are procrastinating (refer to the first user profile). Essentially I need to fit an exponential function to all the users and check whose return value indicates they are showing that most of the action is closer to their last bin. However, I would like to use the Bayesian approach for this analysis. The prior and likelihood should follow Gamma distribution. I am struggling to select what kind of model or method in Baysian I have to implement. Also not sure how to determine exponential or gamma parameters as they are among users. I am open to implementing both R and Python.