This model reaches an absorbing state if it reaches a state and the system remains in that state for all future trials.
This model reaches an absorbing state if it reaches a state and the system remains in that state for all future trials. It can be put in the form of a square matrix with all entries between and including zero and one, with each row summing to one. That form is called a transition matrix. Used to analyze a stochastic process, for ten points, name this probability model developed by a Russian mathematician, which is used in a series of events consiting of a finite number of trials, with a finite number of possible outcomes each having a fixed probability of occurrence, depending only on the result of the previous event.
This model reaches an absorbing state if it reaches a state and the system remains in that state for all future trials.
{Markov chain}