R: Importance sampling using a t proposal density

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 Index]