Fits a density function to a multivariate sample

Fits a density function to a multivariate sample

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)
#> 
 Running DEzs-MCMC, chain  1 iteration 300 of 2502 . Current logp  -13.13027 -13.9082 -12.99289 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 600 of 2502 . Current logp  -17.17077 -15.83237 -14.44773 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 900 of 2502 . Current logp  -14.78237 -12.37018 -13.75772 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 1200 of 2502 . Current logp  -13.2786 -13.84704 -14.69235 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 1500 of 2502 . Current logp  -12.24353 -12.61178 -12.44291 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 1800 of 2502 . Current logp  -11.88731 -13.33655 -14.25091 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 2100 of 2502 . Current logp  -13.29007 -13.83644 -13.27353 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 2400 of 2502 . Current logp  -12.2154 -13.3844 -18.34041 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 2502 of 2502 . Current logp  -18.18653 -14.181 -13.19644 . Please wait! 
#> runMCMC terminated after 0.619999999999891seconds


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)
#> 
 Running DEzs-MCMC, chain  1 iteration 300 of 1002 . Current logp  -7.545238 -7.446456 -7.128085 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 600 of 1002 . Current logp  -8.492681 -7.42916 -7.616632 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 900 of 1002 . Current logp  -8.09548 -7.647625 -7.012468 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 1002 of 1002 . Current logp  -7.20372 -9.717723 -10.57818 . Please wait! 
#> runMCMC terminated after 2.25seconds