Will, are you speaking of Markov chains? Their state transition matrix is
probabilistic, but they can also be in more than one state at a time. We have a
row vector S(t) of state probailities at time t, and a matrix of state
transition proabilities M, where m(i,j) represents the probability of going
from state i to state j. The next state vector S(t+delta t) = S(t) * M. If we
have a continuous time, we get into a set of first-order differential
equations; for a first-order process, where M is time-invariant, we have
first-order differential equations with constant coefficients, leading to
solutions with exponentials in the lack of any driving force. And so on. Is
this applicable?
William Siler
Odium ignorantem est odium infantem,
sed odium savantem est odium ferentem!
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