Monte Carlo Marcov Chain with pymc
Perhaps, assuming each user behaves the same way in a particular time interval, at each interval t we can get the matrix
[ Pr 0->0 , Pr 1->0;
  Pr 1->0 , Pr 1->0]
where Pr x ->y = (the number of people in interval t+1 who are in state y AND who were in state x in interval t) divided by (the number of people who were in state x in interval t), i.e. the probability based on the sample that someone in the given time interval in state x (0 or 1) will transition to state y (0 or 1) in the next time interval.
I'm trying to build a MCMC model to simulate a changing beavior over time. I have to simulate one day with a time interval of 10-minutes. I have several observations of one day from N users in 144 intervals. So I have  U_k=U_1,...,U_N  U users with k ranging from 1 to N and for each user I have X_i=X_1,...X_t samples. Each user has two possible states, 1 and 0. I have understood that I have to build a transition probability matrix for each time step and then run the MCMC model. Is it right? But I did not understood how to build it in pyMC can anybody provided me suggestion?