Creates a standardized prior class

Creates a standardized prior class

createPrior(density = NULL, sampler = NULL, lower = NULL, upper = NULL,
  best = NULL)

Arguments

density	Prior density
sampler	Sampling function for density (optional)
lower	vector with lower bounds of parameters
upper	vector with upper bounds of parameter
best	vector with "best" parameter values

Details

This is the general prior generator. It is highly recommended to not only implement the density, but also the sampler function. If this is not done, the user will have to provide explicit starting values for many of the MCMC samplers. Note the existing, more specialized prior function. If your prior can be created by those, they are preferred. Note also that priors can be created from an existing MCMC output from BT, or another MCMC sample, via createPriorDensity.

Note

min and max truncate, but not re-normalize the prior density (so, if a pdf that integrated to one is truncated, the integral will in general be smaller than one). For MCMC sampling, this doesn't make a difference, but if absolute values of the prior density are a concern, one should provide a truncated density function for the prior.

See also

createPriorDensity createBetaPrior createUniformPrior createTruncatedNormalPrior createBayesianSetup

Examples

# Create a general prior distribution by specifying an arbitrary density function and a
# corresponding sampling function
density = function(par){
  d1 = dunif(par[1], -2,6, log =TRUE)
  d2 = dnorm(par[2], mean= 2, sd = 3, log =TRUE)
  return(d1 + d2)
}

# The sampling is optional but recommended because the MCMCs can generate automatic starting
# conditions if this is provided
sampler = function(n=1){
  d1 = runif(n, -2,6)
  d2 = rnorm(n, mean= 2, sd = 3)
  return(cbind(d1,d2))
}

prior <- createPrior(density = density, sampler = sampler,
                     lower = c(-3,-3), upper = c(3,3), best = NULL)


# Use this prior in an MCMC 

ll <- function(x) sum(dnorm(x, log = TRUE)) # multivariate normal ll
bayesianSetup <- createBayesianSetup(likelihood = ll, prior = prior)

settings = list(iterations = 1000)
out <- runMCMC(bayesianSetup = bayesianSetup, settings = settings)
#> 
 Running DEzs-MCMC, chain  1 iteration 300 of 1002 . Current logp  -7.903088 -7.501712 -6.397311 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 600 of 1002 . Current logp  -7.536071 -6.169692 -6.187793 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 900 of 1002 . Current logp  -6.154527 -7.003082 -8.155503 . Please wait! 

 Running DEzs-MCMC, chain  1 iteration 1002 of 1002 . Current logp  -6.404899 -7.036155 -6.217254 . Please wait! 
#> runMCMC terminated after 0.549999999999727seconds

# see ?createPriorDensity for how to create a new prior from this output