AMCMC

AMCMC is a package written in R and in C, to estimate the expected value of a user-supplied functional with respect to a user-supplied multi-dimensional density function, by performing an adaptive Markov chain Monte Carlo (MCMC) algorithm, specifically adaptive Metropolis-within-Gibbs.


References in zbMATH (referenced in 13 articles , 1 standard article )

Showing results 1 to 13 of 13.
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  1. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  2. van der Linden, Wim J.; Ren, Hao: Optimal Bayesian adaptive design for test-item calibration (2015)
  3. Pasanisi, Alberto; Fu, Shuai; Bousquet, Nicolas: Estimating discrete Markov models from various incomplete data schemes (2012)
  4. Bai, Yan; Roberts, Gareth O.; Rosenthal, Jeffrey S.: On the containment condition for adaptive Markov chain Monte Carlo algorithms (2011)
  5. Robert, Christian P.; Casella, Georges: Monte-Carlo methods with R. Translated from the English by Joachim Robert, Robin Ryder, Arbel, Juyan, Pierre Jacob et Brigitte Plessis. (2011)
  6. Shevchenko, Pavel V.: Modelling operational risk using Bayesian inference. (2011)
  7. Mathur, Sunil K.: Statistical bioinformatics with R. (2010)
  8. Robert, Christian P.; Casella, George: Introducing Monte Carlo methods with R. (2010)
  9. Vihola, Matti: Grapham: graphical models with adaptive random walk Metropolis algorithms (2010)
  10. Jasra, Ajay; Doucet, Arnaud; Stephens, David A.; Holmes, Christopher C.: Interacting sequential Monte Carlo samplers for trans-dimensional simulation (2008)
  11. Gatu, Cristian; Gentle, James; Hinde, John; Huh, Moon: Special issue on statistical algorithms and software (2007)
  12. Rosenthal, Jeffrey S.: AMCMC: an R interface for adaptive MCMC (2007)
  13. Smith, Adrian F. M.: Bayesian computational methods (1991)