Stochastic Search Variable Selection : simulated data example
(George, McCulloch, 1993)

Generate Program

model {for (i  in 1:60) {
                  for (j in 1:5) {
                    x[i,j] ~ dnorm(0,1)}
                    Y[i] ~ dnorm(mu[i],tau)
                     mu[i] <- x[i,4]+1.2*x[i,5]}
                     tau <- 1/(2.5*2.5)}

Analysis Program

model {
        for (i  in 1:n) {     
            Y[i] ~ dnorm(mu[i],tau)
            mu[i] <- b[1]*x[i,1]+b[2]*x[i,2]+b[3]*x[i,3]+b[4]*x[i,4]+b[5]*x[i,5]}
        for (j in 1:5) {
                        g[j] <- gval[j,k]
                        b[j] ~ dnorm(0,tau.b[j])
                        tau.b[j] <- equals(g[j],1)*tau.1+equals(g[j],0)*tau.2}

#Priors:
 
 tau ~ dgamma(0.001,0.001)

# discrete prior on set choices
  for (n in 1:N) {  
                   prior[n] <- 1/N}
                   k ~ dcat(prior[]);    
# alternative precisions           
  tau.1 <- 0.1
  tau.2 <- 10
   
# Specify values taken by g
  gval[1,1] <- 1; gval[2,1] <- 1; gval[3,1] <- 1; gval[4,1] <- 0; gval[5,1] <- 0; 
  gval[1,2] <- 0; gval[2,2] <- 0; gval[3,2] <- 0; gval[4,2] <- 0; gval[5,2] <- 1; 
  gval[1,3] <- 0; gval[2,3] <- 0; gval[3,3] <- 0; gval[4,3] <- 1; gval[5,3] <- 0; 
  gval[1,4] <- 1; gval[2,4] <- 0; gval[3,4] <- 1; gval[4,4] <- 1; gval[5,4] <- 1; 
  gval[1,5] <- 1; gval[2,5] <- 0; gval[3,5] <- 0; gval[4,5] <- 1; gval[5,5] <- 1; 
  gval[1,6] <- 1; gval[2,6] <- 1; gval[3,6] <- 0; gval[4,6] <- 1; gval[5,6] <- 1; 
  gval[1,7] <- 0; gval[2,7] <- 1; gval[3,7] <- 0; gval[4,7] <- 1; gval[5,7] <- 1; 
  gval[1,8] <- 1; gval[2,8] <- 1; gval[3,8] <- 1; gval[4,8] <- 1; gval[5,8] <- 1; 
  gval[1,9] <- 0; gval[2,9] <- 1; gval[3,9] <- 1; gval[4,9] <- 1; gval[5,9] <- 1; 
  gval[1,10] <- 0; gval[2,10] <- 0; gval[3,10] <- 1; gval[4,10] <- 1; gval[5,10] <- 1; 
  gval[1,11] <- 0; gval[2,11] <- 0; gval[3,11] <- 0; gval[4,11] <- 1; gval[5,11] <- 1; 
  gval[1,12] <- 0; gval[2,12] <- 0; gval[3,12] <- 0; gval[4,12] <- 0; gval[5,12] <- 0

comb1 <- equals(k,1)
comb8 <- equals(k,8)
comb2 <- equals(k,2)
comb11 <- equals(k,11)
comb3 <- equals(k,3)
}

Inits (Analysis)
list(tau=1,k=1)

Data  (Analysis)
list(n=60,N=12,Y=c(1.099587321,
-1.274490952,
1.545509577,
6.504482746,
-1.542806149,
3.603931427,
1.212354183,
1.310943604,
2.563740015,
-1.037927508,
3.388747931,
-1.173394799,
2.214322567,
-2.562862873,
-0.615921736,
2.564597368,
-1.899438381,
4.201879978,
1.362431765,
3.615511894,
5.426011086,
0.957006812,
0.434376031,
-4.055436134,
-0.050147418,
5.165518284,
4.115435123,
1.533156395,
3.505029202,
-2.236085653,
-3.056247473,
-1.134916186,
-2.909213066,
1.297193885,
-1.00645411,
1.123415589,
3.907874107,
0.272758037,
-5.539184093,
-3.305099249,
2.744528055,
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1.605427027,
3.319894791,
5.343523502,
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2.104073763,
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0.586278498,
4.442759514,
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-0.426828325,
1.015136719,
2.473385572,
-10.90109539,
0.389823288,
-1.507391691),
x=structure(.Data=c(-1.127980471,
-1.332059741,
0.69610101,
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0.199011356,
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0.240591988,
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1.310551643,
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0.664316535,
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0.398436457,
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