For a continuous-time Markov chain with transition rates qij, detailed balance isfor every pair of distinct states. A probability distribution satisfying these equations is an invariant distribution of a continuous-time Markov chain, because summing over incoming rates gives πQ=0. Under the standard nonexplosion assumptions, the equilibrium chain is reversible.
πiqij=πjqji
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- Balance function of a flow-level network
- Burke theorem for a multi-server queue
- Carrier-sense multiple access
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- Ehrenfest urn model
- Involutive Metropolis proposal
- Last-exit decomposition for a transient random walk
- Metropolis–Hastings algorithm
- Microscopic reversibility
- Model A fluctuation-dissipation relation
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- Poincare inequality for a reversible Markov chain
- Quasireversibility
- Reversible density-ratio propagation
- Spectral kernel expansion of a reversible chain
- Stationary law of a linear flow network
- Systematic Gibbs sampling need not be reversible
- Time reversal of a continuous-time Markov chain
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Detailed balance is a principle used in statistical mechanics and thermodynamics that describes a specific condition of equilibrium in a system. It refers to the condition whereby, for every possible transition between states of a system, the rate of transitions in one direction is balanced by the rate of transitions in the reverse direction. This ensures that, over time, the system reaches a steady-state distribution of states.