impsampling: Importance sampling using a t proposal density

In bayesball/LearnBayes: Learning Bayesian Inference

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)

bayesball/LearnBayes documentation built on May 11, 2019, 9:21 p.m.