impsampling {LearnBayes}R Documentation

Importance sampling using a t proposal density

Description

Implements importance sampling to compute the posterior mean of a function using a multivariate t proposal density

Usage

impsampling(logf,tpar,h,n,data)

Arguments

logf function that defines the logarithm of the density of interest
tpar list of parameters of t proposal density including the mean m, scale matrix var, and degrees of freedom df
h function that defines h(theta)
n number of simulated draws from proposal density
data data and or parameters used in the function logf

Value

est estimate at the posterior mean
se simulation standard error of estimate
theta matrix of simulated draws from proposal density
wt vector of importance sampling weights

Author(s)

Jim Albert

Examples

data(cancermortality)
start=c(-7,6)
fit=laplace(betabinexch,start,cancermortality)
tpar=list(m=fit$mode,var=2*fit$var,df=4)
myfunc=function(theta) return(theta[2])
theta=impsampling(betabinexch,tpar,myfunc,1000,cancermortality)

[Package LearnBayes version 2.0 Index]