To generate a toy data set with 100 observations...
model {
for (i in 1:n) {y[i] ~ dt(mu, r, d)}
}

list(n = 100, mu = 0, r = 1, d = 4)

Model->Save State after gen inits...
list(
y = c(
-0.3010790796947939,0.2705572189131543,0.3311862618866308,-0.1679621601115892,-0.4410951275562629,
-0.841478556370276,-0.6696902820640275,0.4458915529996742,0.7693062900097751,-0.708666651513419,
-1.015606456363378,1.436496993025185,1.836960247521447,0.1905819289430772,-1.177527019161313,
0.2374899583108332,0.7171058063126555,-0.7460512298977264,0.705851552338434,0.1820047649327429,
1.257185369406175,-0.925489448946005,0.09542695952313256,0.8013236782462951,-0.4652032463532497,
0.8841998750373938,-0.06533469909432205,3.297740992995838,-0.6223827333475849,-0.1314369875423831,
-0.2024855317115982,-0.2277574623744996,-1.659924276692189,0.3021881432196167,-0.5454927941072908,
0.7022086418821204,0.2782830144903742,0.9114882738132868,-0.3083386074629043,-0.2966104912788878,
-0.1988373901697388,0.2851484930004808,-0.1426843824299156,-1.4966030202351,-0.3195465568690218,
0.3976198385465806,-1.651798724713947,0.7652039361491948,-0.5724173799437025,-0.2866602918838835,
-0.5781258802658948,-0.1017860278764591,2.002200201883221,0.7804782919811158,0.1743795435615441,
-0.9254609665903467,2.317908877596157,1.634682839874544,0.0832751105435567,-0.2727478701462867,
-0.6797641058026825,-0.4074908711629604,0.732988841661278,1.008126663054926,-0.1277526161229326,
0.09756166360245939,-0.4637063879322463,-0.01935035644652378,-0.4466993463640805,-3.503293968792259,
2.17305776450207,0.2654848253323878,-0.2962892726895821,0.9724351979681869,1.099949483709092,
-0.4012515623733155,-1.32324573905956,-0.750653374328425,0.4069542214066297,-0.7447357366168096,
-0.9307730373668148,-1.120182001314051,-0.3217219941854728,1.645275738087774,-1.228401829289805,
-1.13165213460036,1.710637360396598,0.753272779576963,-0.06421545151949347,-1.566146958042955,
0.03785484978666193,-0.2855985672540369,-0.01517239201025536,1.274252276147554,0.1515816778758929,
2.09858979271664,1.209787533531881,1.014071863848087,0.1394403127569855,0.1265314134760088))

To fit the data...
model {
for (i in 1:n) {y[i] ~ dt(mu, r, d)}
mu ~ dnorm(gamma, inv.omega.squared)
r ~ dgamma(alpha, beta)
d ~ dcat(p[])
p[1] <- 0
for (i in 2:30) {p[i] <- 1/29}
}

Inits:
list(mu = 0, r = 1, d = 10)


Model->Data:
list(n = 100, gamma = 0, inv.omega.squared = 0.0001, alpha = 0.001, beta = 0.001,
y = c(
-0.3010790796947939,0.2705572189131543,0.3311862618866308,-0.1679621601115892,-0.4410951275562629,
-0.841478556370276,-0.6696902820640275,0.4458915529996742,0.7693062900097751,-0.708666651513419,
-1.015606456363378,1.436496993025185,1.836960247521447,0.1905819289430772,-1.177527019161313,
0.2374899583108332,0.7171058063126555,-0.7460512298977264,0.705851552338434,0.1820047649327429,
1.257185369406175,-0.925489448946005,0.09542695952313256,0.8013236782462951,-0.4652032463532497,
0.8841998750373938,-0.06533469909432205,3.297740992995838,-0.6223827333475849,-0.1314369875423831,
-0.2024855317115982,-0.2277574623744996,-1.659924276692189,0.3021881432196167,-0.5454927941072908,
0.7022086418821204,0.2782830144903742,0.9114882738132868,-0.3083386074629043,-0.2966104912788878,
-0.1988373901697388,0.2851484930004808,-0.1426843824299156,-1.4966030202351,-0.3195465568690218,
0.3976198385465806,-1.651798724713947,0.7652039361491948,-0.5724173799437025,-0.2866602918838835,
-0.5781258802658948,-0.1017860278764591,2.002200201883221,0.7804782919811158,0.1743795435615441,
-0.9254609665903467,2.317908877596157,1.634682839874544,0.0832751105435567,-0.2727478701462867,
-0.6797641058026825,-0.4074908711629604,0.732988841661278,1.008126663054926,-0.1277526161229326,
0.09756166360245939,-0.4637063879322463,-0.01935035644652378,-0.4466993463640805,-3.503293968792259,
2.17305776450207,0.2654848253323878,-0.2962892726895821,0.9724351979681869,1.099949483709092,
-0.4012515623733155,-1.32324573905956,-0.750653374328425,0.4069542214066297,-0.7447357366168096,
-0.9307730373668148,-1.120182001314051,-0.3217219941854728,1.645275738087774,-1.228401829289805,
-1.13165213460036,1.710637360396598,0.753272779576963,-0.06421545151949347,-1.566146958042955,
0.03785484978666193,-0.2855985672540369,-0.01517239201025536,1.274252276147554,0.1515816778758929,
2.09858979271664,1.209787533531881,1.014071863848087,0.1394403127569855,0.1265314134760088) )

   node   mean   sd   MC error   2.5%   median   97.5%   start   sample
   d   12.82   7.584   0.1648   3.0   11.0   29.0   1   100000
   mu   0.04393   0.09752   5.455E-4   -0.1467   0.0431   0.2368   1   100000
   r   1.339   0.3203   0.005169   0.8774   1.282   2.123   1   100000