model
	{
	   for(i in 1 : N) {
		Y[i] ~ dnorm(mu[i], tau)
                         mu[i] <- alpha + beta[J[i]] * (x[i] - x.change)		
                         J[i] <- 1 + step(x[i] - x.change)
	   }
	   tau ~ dgamma(0.001, 0.001)
	   alpha ~ dnorm(0.0,1.0E-6)
	   for(j in 1 : 2) {
	   beta[j] ~ dnorm(0.0,1.0E-6)
	   }
	  sigma <- 1 / sqrt(tau)
	  x.change ~ dunif(-1.3,1.1)
	}