Twalk: T-walk MCMC

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

T-walk MCMC

Description

T-walk MCMC

Usage

Twalk(
  bayesianSetup,
  settings = list(iterations = 10000, at = 6, aw = 1.5, pn1 = NULL, Ptrav = 0.4918, Pwalk
    = 0.4918, Pblow = 0.0082, burnin = 0, thin = 1, startValue = NULL, consoleUpdates =
    100, message = TRUE)
)

Arguments

bayesianSetup	Object of class 'bayesianSetup' or 'bayesianOuput'.
settings	list with parameter values.
iterations	Number of model evaluations
at	"traverse" move proposal parameter. Default to 6
aw	"walk" move proposal parameter. Default to 1.5
pn1	Probability determining the number of parameters that are changed
Ptrav	Move probability of "traverse" moves, default to 0.4918
Pwalk	Move probability of "walk" moves, default to 0.4918
Pblow	Move probability of "traverse" moves, default to 0.0082
burnin	number of iterations treated as burn-in. These iterations are not recorded in the chain.
thin	thinning parameter. Determines the interval in which values are recorded.
startValue	Matrix with start values
consoleUpdates	Intervall in which the sampling progress is printed to the console
message	logical determines whether the sampler's progress should be printed

Details

The probability of "hop" moves is 1 minus the sum of all other probabilities.

Value

Object of class bayesianOutput.

Author(s)

Stefan Paul

References

Christen, J. Andres, and Colin Fox. "A general purpose sampling algorithm for continuous distributions (the t-walk)." Bayesian Analysis 5.2 (2010): 263-281.

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