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- Brownian zero mode of a fluctuating interface
- Countable-state Markov chain convergence theorem
- Ehrenfest urn model
- Eigenvalue lower bound for total variation mixing
- Ergodic theorem for a finite continuous-time Markov chain
- Interchange process
- Kac's lemma
- Mean recurrence time
- Pairwise mixing diameter
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- Positive recurrent Markov chain
- Product-form stationary distribution of a loss network
- Random adjacent transposition shuffle
- Random transposition shuffle
- Reversible density-ratio propagation
- Reversible four-cycle flow model
- Separation distance
- Stationary law of a two-resource linear flow network
- Strong stationary time
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A **stationary distribution** is a concept primarily used in the context of Markov chains and stochastic processes. It refers to a probability distribution that remains unchanged as time progresses. In other words, if the system is in the stationary distribution, the probabilities of being in each state do not change over time.