sampleAvg {parallelMCMCcombine}

Sample Averaging Method

Description

The function combines the independent subset posterior samples subchains into the set of samples that estimate the posterior density given the full data set, by averaging the samples across subsets. Individual model parameters are assumed to be independent.

Usage

sampleAvg(subchain, shuff = FALSE)

Arguments

subchain

array of subset posterior samples of the dimension c(d,sampT,M). Here d is the dimension of the parameter space, sampT is the number of samples, and M is the number of subposterior datasets.

shuff

shuff: logical; if TRUE, each of the M subsets of d dimensional parameters in subchain is shuffled.

Details

The array subchain must have dimension c(d,sampT,M). Here d is the dimension of the parameter space, sampT is the number of samples, and M is the number of subposterior datasets.

Value

Returns an array of samples of dimension dim=c(d,sampT) representing an estimated (combined) full posterior density.

Examples

d      <- 2     # dimension of the parameter space  
sampT  <- 1000  # number of subset posterior samples
M      <- 3     # total number of subsets

## simulate Gaussian subposterior samples

theta <- array(NA,c(d,sampT,M)) 

norm.mean <- c(1.0, 2.0)
norm.sd   <- c(0.5, 1.0)

for (i in 1:d)
  for (s in 1:M)        
    theta[i,,s] <- rnorm(sampT, mean=norm.mean[i]+runif(1,-0.01,0.01), sd=norm.sd[i])

## combine samples:

full.theta <- sampleAvg(subchain=theta, shuff=FALSE)