The Adaptive Metropolis Algorithm (Haario et al. 2001)
AM(startValue = NULL, iterations = 10000, nBI = 0, parmin = NULL, parmax = NULL, FUN, f = 1, eps = 0)
| startValue | vector with the start values for the algorithm. Can be NULL if FUN is of class BayesianSetup. In this case startValues are sampled from the prior. |
|---|---|
| iterations | iterations to run |
| nBI | number of burnin |
| parmin | minimum values for the parameter vector or NULL if FUN is of class BayesianSetup |
| parmax | maximum values for the parameter vector or NULL if FUN is of class BayesianSetup |
| FUN | function to be sampled from or object of class bayesianSetup |
| f | scaling factor |
| eps | small number to avoid singularity |
Haario, Heikki, Eero Saksman, and Johanna Tamminen. "An adaptive Metropolis algorithm." Bernoulli (2001): 223-242.