I really like the power of Stan and I have used it for some simpler models so far.
I would like to use Stan for modelling a Marcov like progress. Currently I cannot assess if Stan provides the means to model this type of problem and if yes, what is the right approach to do so?

Process:
Depending on the age of a unit the probability increases that the next status ( broken ) is reached. After a certain time ( repair process) the unit returns to service with a reduced risk of failure, and it starts to age again.
The thing is the process is not memory less, as far as I understand it. Because the probability of failure depends on the age of the unit. But maybe I am interpreting the requirements for a Marcov Chain not correctly.

Do you have a suggestion for an approach in Stan or is it not the right tool for this kind of task?

Stan uses Markov Chain Monte Carlo to obtain samples from a Bayesian posterior distribution. The process that the Bayesian posterior models doesn’t necessarily have anything to do with Markov chains.

Absolutely. That’s what is umder the hood, as far as I red. But is it also possible to use Stan to model the roughly described presses with the benefit of Bayesian inference?

thanks for your replay and sorry for my late response. I have to admit that my current know-how and the resources I am able to devote for this topic are not sufficient to keep it alive.

Nevertheless, I very much appreciate the response and the help provided by you and this forum!

Hi Marv, the short answer is yes you can do this, and there’s a wide variety of options for how to go about doing it… Idk that we have a case study… If all the entry/exit times are observed you may be able to use the joint model in rstanarm to do this which might help you over the hump