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

 Twalk R Documentation

T-walk MCMC

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.

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.