BayesianTools (version 0.1.8)

AM: The Adaptive Metropolis Algorithm

Description

The Adaptive Metropolis Algorithm (Haario et al. 2001)

Usage

AM(
  startValue = NULL,
  iterations = 10000,
  nBI = 0,
  parmin = NULL,
  parmax = NULL,
  FUN,
  f = 1,
  eps = 0
)

Arguments

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

Author

Francesco Minunno

References

Haario, Heikki, Eero Saksman, and Johanna Tamminen. "An adaptive Metropolis algorithm." Bernoulli (2001): 223-242.