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
References in zbMATH (referenced in 12 articles , 1 standard article )
Showing results 1 to 12 of 12.
- Yang, Jinyoung; Rosenthal, Jeffrey S.: Automatically tuned general-purpose MCMC via new adaptive diagnostics (2017)
- van der Linden, Wim J.; Ren, Hao: Optimal Bayesian adaptive design for test-item calibration (2015)
- Pasanisi, Alberto; Fu, Shuai; Bousquet, Nicolas: Estimating discrete Markov models from various incomplete data schemes (2012)
- Bai, Yan; Roberts, Gareth O.; Rosenthal, Jeffrey S.: On the containment condition for adaptive Markov chain Monte Carlo algorithms (2011)
- 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)
- Shevchenko, Pavel V.: Modelling operational risk using Bayesian inference. (2011)
- Robert, Christian P.; Casella, George: Introducing Monte Carlo methods with R. (2010)
- Vihola, Matti: Grapham: graphical models with adaptive random walk Metropolis algorithms (2010)
- Jasra, Ajay; Doucet, Arnaud; Stephens, David A.; Holmes, Christopher C.: Interacting sequential Monte Carlo samplers for trans-dimensional simulation (2008)
- Gatu, Cristian; Gentle, James; Hinde, John; Huh, Moon: Special issue on statistical algorithms and software (2007)
- Rosenthal, Jeffrey S.: AMCMC: an R interface for adaptive MCMC (2007)
- Smith, Adrian F. M.: Bayesian computational methods (1991)