BayesianTools (version 0.1.0)

createPriorDensity: Fits a density function to a multivariate sample

Description

Fits a density function to a multivariate sample

Usage

createPriorDensity(sampler, method = "multivariate", eps = 1e-10,
  lower = NULL, upper = NULL, best = NULL, ...)

Arguments

sampler

an object of class BayesianOutput or a matrix

method

method to generate prior - default and currently only option is multivariate

eps

numerical precision to avoid singularity

lower

vector with lower bounds of parameter for the new prior, independent of the input sample

upper

vector with upper bounds of parameter for the new prior, independent of the input sample

best

vector with "best" values of parameter for the new prior, independent of the input sample

...

parameters to pass on to the getSample function

See Also

createPrior createBetaPrior createTruncatedNormalPrior createUniformPrior createBayesianSetup

Examples

# Create a BayesianSetup
ll <- generateTestDensityMultiNormal(sigma = "no correlation")
bayesianSetup = createBayesianSetup(likelihood = ll, 
                                    lower = rep(-10, 3), 
                                    upper = rep(10, 3))

settings = list(iterations = 2500)
out <- runMCMC(bayesianSetup = bayesianSetup, settings = settings)


newPrior = createPriorDensity(out, method = "multivariate",
                              eps = 1e-10, lower = rep(-10, 3),
                              upper =  rep(10, 3), best = NULL)

bayesianSetup <- createBayesianSetup(likelihood = ll, prior = newPrior)

settings = list(iterations = 1000)
out <- runMCMC(bayesianSetup = bayesianSetup, settings = settings)