I am currently trying to Implement Hidden Markov Models using PyStan. I understand that Stan has a model fitting parameter as n_iter and warmups.
However, I am not able to find the meaning behind the warmups and iterations. I have trained the model and now I am getting the results for 4000 iterations.
Does this mean that last iteration produces the best result or I am missing something?
Warmup --> learn hmc parameters
Iter-warmup --> posterior draws
iter = 2000 per chain
Warmup = 1000 per chain
Draws = 1000 per chain
4 chains --> 4000 draws
Thanks for your response.
Is there any way in Stan wherein I can check the parameters after the warmup. I mean if I want to skip the iteration step and just look at the parameters that were learned from warm up.
With current develop branch you have this option
Older pystan have textual output in