createTruncatedNormalPrior {BayesianTools}

Convenience function to create a truncated normal prior

Description

Convenience function to create a truncated normal prior

Usage

createTruncatedNormalPrior(mean, sd, lower, upper)

Arguments

mean

best estimate for each parameter

sd

sdandard deviation

lower

vector of lower prior range for all parameters

upper

vector of upper prior range for all parameters

Note

for details see createPrior

Author(s)

Florian Hartig

See Also

createPriorDensity
createPrior
createBetaPrior
createUniformPrior
createBayesianSetup

Examples

# the BT package includes a number of convenience functions to specify
# prior distributions, including createUniformPrior, createTruncatedNormalPrior
# etc. If you want to specify a prior that corresponds to one of these
# distributions, you should use these functions, e.g.:

prior <- createUniformPrior(lower = c(0,0), upper = c(0.4,5))

prior$density(c(2, 3)) # outside of limits -> -Inf
prior$density(c(0.2, 2)) # within limits, -0.6931472

# All default priors include a sampling function, i.e. you can create
# samples from the prior via
prior$sampler()
# [1] 0.2291413 4.5410389

# if you want to specify a prior that does not have a default function, 
# you should use the createPrior function, which expects a density and 
# optionally a sampler 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)
}

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(-10,-20), upper = c(10,20), best = NULL)

# note that the createPrior supports additional truncation


# To use a prior in an MCMC, include it in a BayesianSetup 

set.seed(123)
ll <- function(x) sum(dnorm(x, log = TRUE)) # multivariate normal ll
bayesianSetup <- createBayesianSetup(likelihood = ll, prior = prior)

settings = list(iterations = 100)
out <- runMCMC(bayesianSetup = bayesianSetup, settings = settings)

# use createPriorDensity to create a new (estimated) prior from MCMC output

newPrior = createPriorDensity(out, method = "multivariate",
                              eps = 1e-10, lower = c(-10,-20), 
                              upper = c(10,20), best = NULL, scaling = 0.5)