LearnBayes (version 2.15.2)

impsampling: 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)

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

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

Author

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)