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.

Keywords for this software

Anything in here will be replaced on browsers that support the canvas element

References in zbMATH (referenced in 15 articles , 1 standard article )

Showing results 1 to 15 of 15.
Sorted by year (citations)

  1. Giovanna Jona Lasinio; Gianluca Mastrantonio; Mario Santoro: CircSpaceTime: an R package for spatial and spatio-temporal modeling of Circular data (2020) arXiv
  2. Lasinio, Giovanna Jona; Santoro, Mario; Mastrantonio, Gianluca: CircSpaceTime: an R package for spatial and spatio-temporal modelling of circular data (2020)
  3. Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
  4. van der Linden, Wim J.; Ren, Hao: Optimal Bayesian adaptive design for test-item calibration (2015)
  5. Pasanisi, Alberto; Fu, Shuai; Bousquet, Nicolas: Estimating discrete Markov models from various incomplete data schemes (2012)
  6. Bai, Yan; Roberts, Gareth O.; Rosenthal, Jeffrey S.: On the containment condition for adaptive Markov chain Monte Carlo algorithms (2011)
  7. Robert, Christian P.; Casella, George: Monte-Carlo methods with R. Translated from the English by Joachim Robert, Robin Ryder, Arbel, Juyan, Pierre Jacob et Brigitte Plessis. (2011)
  8. Shevchenko, Pavel V.: Modelling operational risk using Bayesian inference. (2011)
  9. Mathur, Sunil K.: Statistical bioinformatics with R. (2010)
  10. Robert, Christian P.; Casella, George: Introducing Monte Carlo methods with R. (2010)
  11. Vihola, Matti: Grapham: graphical models with adaptive random walk Metropolis algorithms (2010)
  12. Jasra, Ajay; Doucet, Arnaud; Stephens, David A.; Holmes, Christopher C.: Interacting sequential Monte Carlo samplers for trans-dimensional simulation (2008)
  13. Gatu, Cristian (ed.); Gentle, James (ed.); Hinde, John (ed.); Huh, Moon (ed.): Editorial: Special issue on statistical algorithms and software (2007)
  14. Rosenthal, Jeffrey S.: AMCMC: an R interface for adaptive MCMC (2007)
  15. Smith, Adrian F. M.: Bayesian computational methods (1991)