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))
| 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 |
Object of class bayesianOutput.
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.