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.

startValue

iterations

minimum values for the parameter vector or NULL if FUN is of class BayesianSetup

parmin

maximum values for the parameter vector or NULL if FUN is of class BayesianSetup

parmax

function to be sampled from or object of class bayesianSetup

interger, determines the frequency with which sampler progress is printed to the console

consoleUpdates

The Metropolis Algorithm (Metropolis et al. 1953)

General-purpose MCMC and SMC samplers, as well as plot and diagnostic functions for Bayesian statistics, with a particular focus on calibrating complex system models. Implemented samplers include various Metropolis MCMC variants (including adaptive and/or delayed rejection MH), the T-walk, two differential evolution MCMCs, two DREAM MCMCs, and a sequential Monte Carlo (SMC) particle filter.

M: The Metropolis Algorithm

Description

Usage

Arguments

References