Model choice using reversible jump Markov chain Monte Carlo

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A guide to Bayesian model selection for ecologists (2015, methodology): “When prior model probabilities are assumed to be equal, the Bayes factor (Bl,l̃) can be computed simply by calculating the quotient of summed number of visits to each model (Ml and Ml̃) in the RJMCMC algorithm (Hastie and Green 2012).”
Estimation of trace gas fluxes with objectively determined basis functions using reversible jump Markov Chain Monte Carlo (2016, methodology): “The rj-MCMC algorithm allows one to propose changes not just to parameter values, but to the parameter dimension as well, and it has been widely used in many fields to address the problem of model selection (Hastie and Green, 2012).”