{ mu.1 ~ dnorm( 0.0, 1.0E-6 ) mu.2 ~ dnorm( 0.0, 1.0E-6 ) sigma ~ dunif( 0.0, 40.0 ) for ( i in 1:n ) { y[ i ] ~ dnorm( mu, tau ) } mu <- mu.1 + mu.2 tau <- 1.0 / ( sigma * sigma ) y.predicted ~ dnorm( mu, tau ) }