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The Gelman-Rubin statistic allows a statement about the convergence of Monte Carlo simulations.
Monte Carlo simulations (chains) are started with different initial values. The samples from the respective burn-in phases are discarded. From the samples (of the j-th simulation), the variance between the chains and the variance in the chains is estimated:
An estimate of the Gelman-Rubin statistic then results as [1]
When L tends to infinity and B tends to zero, R tends to 1.
The Geweke Diagnostic compares whether the mean of the first x percent of a chain and the mean of the last y percent of a chain match.[ citation needed]
![]() | This article needs attention from an expert in Mathematics. Please add a reason or a talk parameter to this template to explain the issue with the article.(January 2024) |
The Gelman-Rubin statistic allows a statement about the convergence of Monte Carlo simulations.
Monte Carlo simulations (chains) are started with different initial values. The samples from the respective burn-in phases are discarded. From the samples (of the j-th simulation), the variance between the chains and the variance in the chains is estimated:
An estimate of the Gelman-Rubin statistic then results as [1]
When L tends to infinity and B tends to zero, R tends to 1.
The Geweke Diagnostic compares whether the mean of the first x percent of a chain and the mean of the last y percent of a chain match.[ citation needed]