model 
{
	for (i in 1:N) {
		Y[i] ~ dnorm(mu[i], nugget.prec)
		mu[i] <- beta + W[i]
		muW[i] <- 0.0
	}
	
	nugget.prec~ dgamma(0.10,0.10)
	beta ~ dnorm(0.0, 0.0001)
	tausq <- 1/nugget.prec

	W[1:N] ~ spatial.exp(muW[], x[], y[], spatial.prec, phi, 1) 	
	phi ~ dgamma(0.1,0.1)	
	
	spatial.prec ~ dgamma(0.10, 0.10)
	sigmasq <- 1/spatial.prec
	
#    Predictions Independent
#	for (j in 1:M) {
#          muW0[j] <- 0
#		W0[j] ~ spatial.unipred(muW0[], x0[], y0[], W[])
#		Y0[j] <- beta + W0[j]
#	}
	
	# Predictions Joint
	W0[1:M] ~ spatial.pred(muW0[], x0[], y0[], W[])
	for(j in 1:M) {
		muW0[j] <- 0
		Y0[j] <- beta + W0[j]
	}
}

#Initial Values
list(beta=0,phi=1.0,spatial.prec=1.0,nugget.prec=1.0)
list(beta=5,phi=2.0,spatial.prec=2.0,nugget.prec=2.0)
list(beta=-5,phi=0.50,spatial.prec=3.0, nugget.prec=3.0)

list(W=c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0))

list(W0=c(0, 0, 0, 0, 0))

#Data
list(N=50, M=5)

x0[] y0[]
50 50
35 35
40 60
30 70
90 90
END

x[]	y[]	Y[]
11.3703411305323 7.37798800691962 156.436241588382
62.2299404814839 30.9686601860449 130.608501750767
60.9274732880294 71.727174334228 147.135933050089
62.3379441676661 50.4545912146568 135.613372960662
86.0915383556858 15.2998958947137 140.357498671987
64.0310605289415 50.3933488158509 138.365483983678
0.94957563560456 49.3960923049599 145.389770192946
23.2550506014377 75.1200197031721 151.076127447939
66.6083758231252 17.4649823922664 138.416897667159
51.4251141343266 84.8392410436645 146.836179132390
69.3591291783378 86.483383201994 140.606745437303
54.4974835589528 4.18572751805186 135.471762084700
28.2733583590016 31.7182155326009 135.265046991444
92.3433484276757 1.37499391566962 138.707824395499
29.2315840255469 23.9025726681575 138.980061544737
83.7295628152788 70.6494617275894 154.248738826453
28.6223284667358 30.8094757143408 135.520427081491
26.6820780001581 50.8547565666959 140.377354575042
18.6722789658234 5.16466193366796 161.306447592348
23.2225910527632 56.4569839974865 141.217101419074
31.6612454829738 12.1480187168345 137.993900639570
30.2693370729685 89.2836381681263 147.842664038983
15.9046002896503 1.46272557321936 163.322049945899
3.99959180504084 78.3121103653684 143.045388916187
21.8799541005865 8.99613329675049 158.921839298305
81.0598552459851 51.9189980812371 146.781344757297
52.5697546778247 38.4266687557101 135.765257613415
91.4658166002482 7.00524973217398 128.952447672909
83.134504687041 32.064442220144 143.922422788703
4.5770263299346 66.8495397083461 144.572964773944
45.6091482425109 92.6400476368144 149.363892505434
26.5186671866104 47.1909721149132 143.243227144996
30.4672203026712 14.2615343211219 131.261596121353
50.730687007308 54.4269755017012 141.354093385672
18.1096208281815 19.6174651850015 143.504921454932
75.9670635452494 89.858048921451 141.621564901368
20.1248037628829 38.9499784680083 149.642022572228
25.880981865339 31.0870779678226 134.865238569804
99.2150417529047 16.0028662998229 150.404005513985
80.7352340314537 89.6185849560425 142.289083969026
55.3333590738475 16.639378038235 133.987868456348
64.6406094077975 90.0424596155062 147.887614175589
31.1824307078496 13.4078195085749 143.089272902873
62.1819198131561 13.1614134181291 140.514656767181
32.9770175740123 10.5287502519786 140.962329891474
50.1997472951189 51.1583581101149 136.520436710540
67.7094527287409 30.0199053948745 143.399259447736
48.4991239150986 2.67168954014778 137.737902483566
24.3928827345371 30.9647431364283 145.299994259224
76.5459787566215 74.211965710856 156.27404638712
END