Skip to content
Probability II / Continuous-Time Markov Chains and Queueing Theory

Limiting Behaviour: Stationary Distributions for Continuous-Time Chains

Discrete-time convergence to equilibrium needed a genuine hypothesis — aperiodicity — that periodic chains fail. The continuous-time story is, surprisingly, simpler: the randomness of exponential holding times automatically rules out any periodicity-style obstruction, so irreducibility alone (plus a stationary distribution) is enough to guarantee convergence. This note carries the discrete-chains topic's stationary-distribution and detailed-balance machinery over to continuous time, replacing πp=π\pi p=\pi with the generator-matrix condition πQ=0\pi Q=0.
Learning objectives. By the end of this note you should be able to:
  • state irreducibility for a CTMC and explain why aperiodicity is automatic;
  • state and use the πQ=0\pi Q=0 criterion for stationarity;
  • state the convergence theorem and the detailed balance condition for CTMCs;
  • derive the birth-death stationary-distribution formula.

In this lesson

  1. Irreducibility and the disappearance of periodicity
  2. Stationary distributions
  3. Detailed balance for CTMCs

The full lesson, with worked examples and exercise solutions, is for members.

Read the full lesson

New to SelfMadeQuant.com? See membership options or explore the curriculum.