In the mathematical theory of probability, an absorbing Markov chain is a Markov chain in which every state can reach an absorbing state. An absorbing state is a state that, once entered, cannot be left. Like general Markov chains, there can be continuous-time absorbing Markov chains with an infinite state space. However, this article concentrates on the discrete-time discrete-state-space case.
Formal definition
A Markov chain is an absorbing chain if
there is at least one absorbing state and
it is possible to go from any state to at least one absorbing state in a finite number of steps.
In an absorbing Markov chain, a state that is not absorbing is called transient.
Canonical form
Let an absorbing Markov chain with transition matrix P have t transient states and r absorbing states. Then where Q is a t-by-t matrix, R is a nonzero t-by-r matrix, 0 is an r-by-tzero matrix, and Ir is the r-by-r identity matrix. Thus, Q describes the probability of transitioning from some transient state to another while R describes the probability of transitioning from some transient state to some absorbing state.
Fundamental matrix
A basic property about an absorbing Markov chain is the expected number of visits to a transient state j starting from a transient state i. The probability of transitioning from i to j in exactly k steps is the -entry of Qk. Summing this for all k yields the fundamental matrix, denoted by N. It can be proven that where It is the t-by-t identity matrix. The entry of matrix N is the expected number of times the chain is in state j, given that the chain started in state i. With the matrixN in hand, other properties of the Markov chain are easy to obtain.
Variance on number of visits
The variance on the number of visits to a transient state j with starting at a transient state i is the -entry of the matrix where Ndg is the diagonal matrix with the same diagonal as N and Nsq is the Hadamard product of N with itself.
The expected number of steps before being absorbed when starting in transient state i is the ith entry of the vector where 1 is a length-tcolumn vector whose entries are all 1.
Variance on number of steps
The variance on the number of steps before being absorbed when starting in transient state i is the ith entry of the vector where tsq is the Hadamard product of t with itself.
Transient probabilities
The probability of visiting transient state j when starting at a transient state i is the -entry of the matrix where Ndg is the diagonal matrix with the same diagonal as N.
Absorbing probabilities
Another property is the probability of being absorbed in the absorbing state j when starting from transient state i, which is the -entry of the matrix
Examples
String generation
Consider the process of repeatedly flipping a fair coin until the sequence appears. This process is modeled by an absorbing Markov chain with transition matrix The first state represents the empty string, the second state the string "H", the third state the string "HT", and the fourth state the string "HTH". Although in reality, the coin flips cease after the string "HTH" is generated, the perspective of the absorbing Markov chain is that the process has transitioned into the absorbing state representing the string "HTH" and, therefore, cannot leave. For this absorbing Markov chain, the fundamental matrix is The expected number of steps starting from each of the transient states is Therefore, the expected number of coin flips before observing the sequence is 10, the entry for the state representing the empty string.
Games of chance
Games based entirely on chance can be modeled by an absorbing Markov chain. A classic example of this is the ancient Indian board gameSnakes and Ladders. The graph on the right plots the probability mass in the lone absorbing state that represents the final square as the transition matrix is raised to larger and larger powers. To determine the expected number of turns to complete the game, compute the vector t as described above and examine tstart, which is approximately 39.2.
The example of infectious disease testing, in either blood products or in medical clinics, is often taught as an example of an absorbing Markov chain. The public U.S. Centers for Disease Control and Prevention model for HIV and for hepatitis B, for example, illustrates the property that absorbing Markov chains can lead to the detection of disease, versus the loss of detection through other means. In the standard CDC model, the Markov chain has five states, a state in which the individual is uninfected, then a state with infected but undetectable virus, a state with detectable virus, and absorbing states of having quit/been lost from the clinic, or of having been detected. The typical rates of transition between the Markov states are the probability p per unit time of being infected with the virus, w for the rate of window period removal, q for quit/loss rate from the system, and d for detection, assuming a typical rate at which the health system administers tests of the blood product or patients in question. It follows that we can "walk along" the Markov model to identify the overall probability of detection for a person starting as undetected, by multiplying the probabilities of transition to each next state of the model as: The subsequent total absolute number of false negative tests—the primary CDC concern—would then be the rate of tests, multiplied by the probability of reaching the infected but undetectable state, times the duration of staying in the infected undetectable state:
