dens.prior: Density of the prior

In HPbayes: Heligman Pollard mortality model parameter estimation using Bayesian Melding with Incremental Mixture Importance Sampling

Description

This function calculates the density of the prior distribution for the eight parameters of the Heligman Pollard model. The density is calculated using a uniform distribution. The lower bounds all default to 0 except parameter F, which has a default lower bound of 15. The upper bounds default to A-0.15, B-1, C-1, D-0.25, E-15, F-55, G-0.1, F-1.25.

Usage

dens.prior(x, pri.lo = c(0, 0, 0, 0, 0, 15, 0, 0), 
pri.hi = c(0.15, 1, 1, 0.25, 15, 55, 0.1, 1.25))

Arguments

x	A 1 x 8 vector or n x 8 matrix containing values for the eight Heligman-Pollard Parameters
pri.lo	A vector giving the lower bounds of the uniform prior. Defaults to 0 for all parameters except F which has a default lower bound of 15.
pri.hi	A vector giving the upper bounds of the uniform prior. Defaults to A-0.15, B-1, C-1, D-0.25, E-15, F-55, G-0.1, F-1.25.

Value

A scalar describing the density of the prior distribution

Examples

#Generate a prior distribution
pri.lo = c(0, 0, 0, 0, 0, 15, 0, 0)
pri.hi = c(0.15, 1, 1, 0.25, 15, 55, 0.1, 1.25)
B0 <- 8000
q0 <- cbind(runif(B0, pri.lo[1], pri.hi[1]), 
runif(B0, pri.lo[2], pri.hi[2]), 
runif(B0, pri.lo[3], pri.hi[3]), 
runif(B0, pri.lo[4], pri.hi[4]), 
runif(B0, pri.lo[5], pri.hi[5]), 
runif(B0, pri.lo[6], pri.hi[6]), 
runif(B0, pri.lo[7], pri.hi[7]), 
runif(B0, pri.lo[8], pri.hi[8]))

density <- dens.prior(x=q0)

HPbayes documentation built on May 2, 2019, 5:53 a.m.