AM: The Adaptive Metropolis Algorithm

In BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics

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(s)

Francesco Minunno

References

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

BayesianTools documentation built on Feb. 16, 2023, 8:44 p.m.