Grapham: graphical models with adaptive random walk Metropolis algorithms. Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementation covering several such methods, with emphasis on graphical models for directed acyclic graphs. The implemented algorithms include the seminal Adaptive Metropolis algorithm adjusting the proposal covariance according to the history of the chain and a Metropolis algorithm adjusting the proposal scale based on the observed acceptance probability. Different variants of the algorithms allow one, for example, to use these two algorithms together, employ delayed rejection and adjust several parameters of the algorithms. The implemented Metropolis-within-Gibbs update allows arbitrary sampling blocks. The software is written in C and uses a simple extension language Lua in configuration.
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References in zbMATH (referenced in 4 articles )
Showing results 1 to 4 of 4.
- Pasanisi, Alberto; Fu, Shuai; Bousquet, Nicolas: Estimating discrete Markov models from various incomplete data schemes (2012)
- Vihola, Matti: Robust adaptive Metropolis algorithm with coerced acceptance rate (2012)
- Bai, Yan; Roberts, Gareth O.; Rosenthal, Jeffrey S.: On the containment condition for adaptive Markov chain Monte Carlo algorithms (2011)
- Vihola, Matti: Grapham: graphical models with adaptive random walk Metropolis algorithms (2010)