This function returns
TRUE if the object is stationary
according to the
Geweke.Diagnostic function, and
This is a vector, matrix, or object of class
Stationarity, here, refers to the limiting distribution in a Markov chain. A series of samples from a Markov chain, in which each sample is the result of an iteration of a Markov chain Monte Carlo (MCMC) algorithm, is analyzed for stationarity, meaning whether or not the samples trend or its moments change across iterations. A stationary posterior distribution is an equilibrium distribution, and assessing stationarity is an important diagnostic toward inferring Markov chain convergence.
In the cases of a matrix or an object of class
Markov chains (as column vectors) must be stationary for
is.stationary to return
Alternative ways to assess stationarity of chains are to use the
is.stationary returns a logical value indicating whether or not
the supplied object is stationary according to the
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