model {

  mu ~ dunif( -2, 5 )
  sigma ~ dunif( 0, 6 )

  for ( i in 1:k ) {

    theta[ i ] ~ dnorm( mu, tau.theta )
    y[ i ] ~ dnorm( theta[ i ], tau.y[ i ] )

  }

  tau.theta <- 1.0 / ( sigma * sigma )

  positive.effect <- step( mu )

}