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In mathematics and statistical mechanics, a Markov process is said to show detailed balance if the transition rates between each pair of states i and j in the state space obey
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where P is the Markov transition matrix (transition probability), ie Pij = P( Xt =j | Xt−1 = i ); and πi and πj are the equilibrium probabilities of being in states i and j, respectively.
The definition carries over straightforwardly to continuous variables, where π becomes a probability density, and P a transition kernel:
A Markov process that satisfies the detailed balance equations is said to be a reversible Markov process or reversible Markov chain with respect to π.
Note that the detailed balance condition is stronger than that required merely for a stationary distribution. It applies separately pairwise to each pair of states, so a steady-state probability current A -> B -> C -> A does not suffice.
Detailed balance is a weaker condition than requiring the transition matrix be symmetric, Pij = Pji. That would imply that the uniform distribution over the states would automatically be an equilibrium distribution. However, for continuous systems it may be possible to continuously transform the co-ordinates until a uniform metric is the equilibrium distribution, with a transition kernel which then is symmetric. In the discrete case it may be possible to achieve something similar, by breaking the Markov states into a degeneracy of sub-states.
Such an invariance is a supporting justification for the principle of equal a-priori probability in statistical mechanics.
|This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Detailed_balance". A list of authors is available in Wikipedia.