			model {

			# Spatially structured multivariate normal likelihood  
			# exponential correlation function
				height[1:N] ~ spatial.exp(mu[], x[], y[], tau, phi, kappa)
			# disc correlation function 
			#	height[1:N] ~ spatial.disc(mu[], x[], y[], tau, alpha)

				for(i in 1:N) {
					mu[i] <- beta
				}

			# Priors
				beta ~ dflat()
				tau ~ dgamma(0.001, 0.001) 
				sigma2 <- 1/tau

			# priors for spatial.exp parameters
			# prior range for correlation at min distance (0.2 x 50 ft) is 0.02 to 0.99
				phi ~ dunif(0.05, 20)
			# prior range for correlation at max distance (8.3 x 50 ft) is 0 to 0.66
				kappa ~ dunif(0.05,1.95)

			# priors for spatial.disc parameter
			# prior range for correlation at min distance (0.2 x 50 ft) is 0.07 to 0.96
			#	alpha ~ dunif(0.25, 48)
			# prior range for correlation at max distance (8.3 x 50 ft) is 0 to 0.63

			# Spatial prediction

			# Single site prediction
				for(j in 1:M) {
					height.pred[j] ~ spatial.unipred(beta, x.pred[j], y.pred[j], height[])
				}

			# Only use joint prediction for small subset of points, due to length of time it takes to run
				for(j in 1:10) { mu.pred[j] <- beta }
					height.pred.multi[1:10] ~ spatial.pred(mu.pred[], x.pred[1:10], y.pred[1:10], height[])   
				}
			}
