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:<math>\pi_{i} P_{ij} = \pi_{j} P_{ji}\,,</math>
:<math>\pi_{i} P_{ij} = \pi_{j} P_{ji}\,,</math>


where <math>P</math> is the Markov transition matrix (transition probability), ''i.e.'', ''P''<sub>''ij''</sub>&nbsp;=&nbsp;''P''(''X''<sub>''t''</sub>&nbsp;=&nbsp;''j''&nbsp;|&nbsp;''X''<sub>''t''&nbsp;−&nbsp;1</sub>&nbsp;=&nbsp;''i''); and π<sub>''i''</sub> and π<sub>''j''</sub> are the equilibrium probabilities of being in states ''i'' and ''j'', respectively.<ref name=OHagan>Anthony O'Hagan and Jonathan J. Forster "Kendall's Advanced Theory of Statistics, Volume 2B: Bayesian Inference", Wiley (2004) ISBN 0340807520</ref>  When Pr(''X''<sub>''t''−1</sub>&nbsp;=&nbsp;''i'')&nbsp;=&nbsp;π<sub>''i''</sub> for all ''i'', this is equivalent to the joint probability matrix, Pr(''X''<sub>''t''−1</sub>&nbsp;=&nbsp;''i'',&nbsp;''X''<sub>''t''</sub>&nbsp;=&nbsp;''j'') being symmetric in ''i'' and ''j''; or symmetric in ''t''&nbsp;−&nbsp;1 and&nbsp;''t''.
where <math>P</math> is the Markov transition matrix (transition probability), ''i.e.'', ''P''<sub>''ij''</sub>&nbsp;=&nbsp;''P''(''X''<sub>''t''</sub>&nbsp;=&nbsp;''j''&nbsp;|&nbsp;''X''<sub>''t''&nbsp;−&nbsp;1</sub>&nbsp;=&nbsp;''i''); and π<sub>''i''</sub> and π<sub>''j''</sub> are the equilibrium probabilities of being in states ''i'' and ''j'', respectively.<ref name=OHagan>Anthony O'Hagan and Jonathan J. Forster "Kendall's Advanced Theory of Statistics, Volume 2B: Bayesian Inference", Oxford University Press (2004) ISBN 0340807520</ref>  When Pr(''X''<sub>''t''−1</sub>&nbsp;=&nbsp;''i'')&nbsp;=&nbsp;π<sub>''i''</sub> for all ''i'', this is equivalent to the joint probability matrix, Pr(''X''<sub>''t''−1</sub>&nbsp;=&nbsp;''i'',&nbsp;''X''<sub>''t''</sub>&nbsp;=&nbsp;''j'') being symmetric in ''i'' and ''j''; or symmetric in ''t''&nbsp;−&nbsp;1 and&nbsp;''t''.


The definition carries over straightforwardly to continuous variables, where π becomes a probability density, and ''P''(''s''′,&nbsp;''s'') a transition kernel probability density from state ''s''′ to state&nbsp;''s'':
The definition carries over straightforwardly to continuous variables, where π becomes a probability density, and ''P''(''s''′,&nbsp;''s'') a transition kernel probability density from state ''s''′ to state&nbsp;''s'':
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