M: The Metropolis Algorithm

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

The Metropolis Algorithm

Description

The Metropolis Algorithm (Metropolis et al. 1953)

Usage

M(
  startValue = NULL,
  iterations = 10000,
  nBI = 0,
  parmin = NULL,
  parmax = NULL,
  f = 1,
  FUN,
  consoleUpdates = 1000
)

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
f	scaling factor
FUN	function to be sampled from or object of class bayesianSetup
consoleUpdates	interger, determines the frequency with which sampler progress is printed to the console

Author(s)

Francesco Minunno

References

Metropolis, Nicholas, et al. "Equation of state calculations by fast computing machines." The journal of chemical physics 21.6 (1953): 1087-1092.

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