{

  mu ~ dnorm( 0.0, 1.0E-6 )
  sigma ~ dunif( 0.0, 9.0 )

  for ( i in 1:n ) {

    y[ i ] ~ dnorm( mu, tau )

    log.predictive.density[ i ] <- 
      - pow( y[ i ] - mu, 2 ) /
      ( 2 * pow( sigma, 2 ) )
      - log( sigma ) - 0.9189385
  }

  tau <- 1.0 / ( sigma * sigma )

  y.new ~ dnorm( mu, tau )

  approximate.ls.fs <- 
    mean( log.predictive.density[ ] )

}