R package. mcmc: Markov Chain Monte Carlo. Simulates continuous distributions of random vectors using Markov chain Monte Carlo (MCMC). Users specify the distribution by an R function that evaluates the log unnormalized density. Algorithms are random walk Metropolis algorithm (function metrop), simulated tempering (function temper), and morphometric random walk Metropolis (Johnson and Geyer, Annals of Statistics, 2012, function morph.metrop), which achieves geometric ergodicity by change of variable.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
- Johnson, Leif T.; Geyer, Charles J.: Variable transformation to obtain geometric ergodicity in the random-walk Metropolis algorithm (2012)
- Okabayashi, Saisuke; Geyer, Charles J.: Long range search for maximum likelihood in exponential families (2012)
- Rosenthal, Jeffrey S.: AMCMC: an R interface for adaptive MCMC (2007)