gmGS {ctmcd} | R Documentation |
Gibbs Sampler
Description
Function for deriving a Markov generator matrix estimate by Gibbs sampling (described by Bladt and Soerensen, 2005)
Usage
gmGS(tmabs, te, prior, burnin, conv_pvalue = 0, conv_freq = 10,
niter = 10000, sampl_method = "Unif", expmethod = "PadeRBS", verbose = FALSE,
combmat=NULL, sampl_func = NULL)
Arguments
tmabs |
matrix of absolute transition frequencies |
te |
time elapsed in transition process |
prior |
list of prior parameters (Gamma prior) |
burnin |
number of burn-in iterations |
conv_pvalue |
convergence criterion: stop, if Heidelberger and Welch's diagnostic assumes convergence (see coda package), convergence check is only employed if conv_pvalue>0 |
conv_freq |
convergence criterion: absolute frequency of convergence evaluations |
niter |
stop criterion: stop, if maximum number of iterations is exceeded |
sampl_method |
method for sampling paths from endpoint-conditioned Markov processes. options: "Unif" - Uniformization sampling, "ModRej" - Modified Rejection Sampling |
expmethod |
method for computation of matrix exponential, by default "PadeRBS" is chosen (see |
verbose |
verbose mode |
combmat |
matrix specifying the combined use of sampling methods: "U" - uniformization sampling, "M" - modified rejection sampling |
sampl_func |
interface for own endpoint-conditioned Markov process sampling function |
Details
A posterior mean generator matrix estimate is derived by Gibbs Sampling. The gamma distribution is used as prior.
Value
generator matrix estimate
Author(s)
Marius Pfeuffer
References
M. Bladt and M. Soerensen: Statistical Inference for Discretely Observed Markov Jump Processes. Journal of the Royal Statistical Society B 67(3):395-410, 2005
See Also
rNijTRiT_ModRej
, rNijTRiT_Unif
Examples
data(tm_abs)
## Example prior parametrization (absorbing default state)
pr=list()
pr[[1]]=matrix(1,8,8)
pr[[1]][8,]=0
pr[[2]]=c(rep(5,7),Inf)
## Derive Gibbs sampling generator matrix estimate
gmgs=gmGS(tmabs=tm_abs,te=1,sampl_method="Unif",prior=pr,burnin=10,niter=100,verbose=TRUE)
gmgs