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 2.15.1 Index]