Twalk {BayesianTools}

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.