{

  mu ~ dnorm( 0.0, 1.0E-6 )
  sigma ~ dunif( 0.0, 9.0 )
  nu ~ dunif( 2.0, 12.0 )

  for ( i in 1:n ) {

    y[ i ] ~ dt( mu, tau, nu )

    log.predictive.density[ i ] <-
      loggam( ( nu + 1 ) / 2 ) - ( ( nu + 1 ) / 2 ) * 
      log( 1 + pow( y[ i ] - mu, 2 ) / 
      ( nu * pow( sigma, 2 ) ) ) - loggam( nu / 2 ) - 
      log( nu * 3.141593 ) / 2 - log( sigma )

  }

  tau <- 1.0 / ( sigma * sigma )

  y.new ~ dt( mu, tau, nu )

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

}