Exapting the Markov Model

Repurpose a state-transition model for a new domain while checking the adequacy of its state definitions and conditional-independence assumption.

Exapting a Markov model means repurposing its state-and-transition structure for a new problem. Instead of tracking a complete narrative, define a state that contains the information needed to predict the next transition, then estimate probabilities of moving between states.

The crucial condition is not that history has no influence. It is that, given the chosen present state, earlier history adds no further predictive information under the model. A state can itself include recent history or other relevant variables. Reuse the mathematical structure only after checking whether the new domain supports its assumptions; a familiar diagram does not supply valid transition probabilities.

When to use it

Useful when transferring probabilistic modeling tools between domains with observable transitions.

How it can help

Use explicit states and transition probabilities to investigate a process, then test the approximation.