T-walk MCMC
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)
)Object of class bayesianOutput.
Object of class 'bayesianSetup' or 'bayesianOuput'.
list with parameter values.
Number of model evaluations
"traverse" move proposal parameter. Default to 6
"walk" move proposal parameter. Default to 1.5
Probability determining the number of parameters that are changed
Move probability of "traverse" moves, default to 0.4918
Move probability of "walk" moves, default to 0.4918
Move probability of "traverse" moves, default to 0.0082
number of iterations treated as burn-in. These iterations are not recorded in the chain.
thinning parameter. Determines the interval in which values are recorded.
Matrix with start values
Intervall in which the sampling progress is printed to the console
logical determines whether the sampler's progress should be printed
Stefan Paul
The probability of "hop" moves is 1 minus the sum of all other probabilities.
Christen, J. Andres, and Colin Fox. "A general purpose sampling algorithm for continuous distributions (the t-walk)." Bayesian Analysis 5.2 (2010): 263-281.