Absorbing Markov Chain Interview Guide
Absorbing Markov chain interview guide for absorbing states, transient states, absorption probability, expected time, examples, and mistakes.
Candidates solving finite-state probability and process questions.
Absorbing states do not leave
An absorbing state has probability one of staying in itself once reached. Absorbing Markov chain questions ask about whether and when a path reaches those states.
Transient states lead somewhere
Transient states can be visited before absorption. Setting up equations for absorption probabilities or expected times is often cleaner than simulating by hand.
Concrete example
In gambler’s ruin, zero wealth and target wealth can be absorbing states. Starting wealth determines the probability of hitting the target before ruin.
Boundary conditions solve the problem
Set absorption probabilities at absorbing states first, then write recursion for transient states. The boundary values anchor the system.
Common mistakes
Candidates often forget to mark absorbing states explicitly. A strong answer names absorbing and transient states before writing transition equations.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.