i) Markov process is a sequence of events in which the probability of occurrence for one event depends upon the preceding event. It is time-based process. For example a patients state today depends on previous days state
ii) Cyclic chain is one that repeats itself in a deterministic manner. The transition matrix has one?s in two or more rows that form a closed path among cycle states. Example is a machine operation that repeats itself
iii) Absorbing state is one that cannot be left once entered. It has a transition probability of one to itself and zero to other states. Example includes the payment of a bill, sale of a capital asset or termination of an employee
iv) State transition matrix is a rectangular array that summarises the transition probabilities for a given Markov process. Transition probabilities are the probabilities of occurrence of each event depending on the state of the generator
v) Steady state is the condition that in the long run period of time a system settles or stabilizes to.
Wilfykil answered the question on February 20, 2019 at 10:04
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