Markov Chain Monte Carlo convergence diagnostics in Julia
Author tpapp
22 Stars
Updated Last
1 Year Ago
Started In
June 2017


MCMCDiagnostics.jl has been deprecated in favor of MCMCDiagnosticTools.jl and is no longer maintained.

Markov Chain Monte Carlo convergence diagnostics in Julia.

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This package contains two very useful diagnostics for Markov Chain Monte Carlo:

  1. potential_scale_reduction(chains...), which estimates the potential scale reduction factor, also known as Rhat, for multiple scalar chains,

  2. effective_sample_size(chain), which calculates the effective sample size for scalar chains.

These are intended as building blocks, to be used by other libraries, and were organized into a separate library for testing and DRY.


The package is registered. You can install it with



You may find my other packages for MCMC interesting. See the documentation of DynamicHMC.jl for details.


Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using multiple sequences. Statistical science, 457-472.

Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2013). Bayesian data analysis (3rd edition). Chapman & Hall/CRC.

Stan Development Team. (2017). Stan Modeling Language Users Guide and Reference Manual, Version 2.15.0.