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I have looked for an answer for a while, but with no luck. I am trying to develop a discrete time Markov model. Presently, I have 5 states, with the 5th state being the absorbing state. I also know the variable time durations that each state stays in. So say state one might take 20 years to transition to state 2 and this is normally distributed. Is there a way to calculate the transition matrix with this data?

update: I'm thinking along the lines of Monte-carlo Markov chain simulation, but I'm unsure how to structure the MCMC model.

Simon Bush
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Have you considered taking into account only the first occurrence of each state in your data? This way you can populate your transition matrix using the obtained Markov chain with no consecutive identical states.

Mera
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  • Thanks for replying - in the end I changed my methodology slightly and went with a semi-markov approach. This allowed my to simulate sojourn times based on expert gathered data. I then compared the sojourn time with modelled clock time to say when my state changed. – Simon Bush Apr 13 '15 at 06:01