We have a dataset on the self-evaluation health state on random days. The health state of the patients (agents) depends on the receiving the service or not.
Basically, we have the sequence of the health state for each patient which is only known at specific days (not necessary equidistance from each other). The state is known when the agent receives the service.
We need to figure out the transition probability for each patient (class of patients) when they receive and do not receive the service.
What are the options for estimating the transition parameters? Pattern recognition? regression? ....
We have a dataset on the self-evaluation health state on random days. The health state of the patients (agents) depends on the receiving the service or not.
Basically, we have the sequence of the health state for each patient which is only known at specific days (not necessary equidistance from each other). The state is known when the agent receives the service.
We need to figure out the transition probability for each patient (class of patients) when they receive and do not receive the service.
What are the options for estimating the transition parameters? Pattern recognition? regression? ....