how to obtain posterior predictives in rjags?

Question

I would like to create a plot like this, where y is the observed data and yrep is the predictions using rjags:

I looked at the code from the example here: JAGS and R: Obtain posterior predictive distribution for specific x

but I don't understand why they use:

for(i in 1:3){
    yP[i] ~ dt(intercept+slope*xP[i],tau,nu)
}

specifically, why are they using

dt()

If my overall model looks like this:

jagsscript <- cat("
model {

###priors####

    alpha.m ~ dunif(1,100);
    beta.m ~ dunif(0,1);

    
    s.a.sd ~ dunif(0,3);
    s.b.sd ~ dunif(0,3);

    
    sigmaObs ~ dunif(0,2);

    for(i in 1:Ndata){
    
      pRR[i] <- alpha[i] * SS[i] * exp(-1*beta[i]*SS[i]); 
      lRR[i] ~ dnorm(log(pRR[i]) - (pow(sigmaObs,2)/2),pow(sigmaObs,-2));       
      
      lalpha[i] <- log(alpha.m) + s.a[stream[i]];
      lbeta[i] <- log(beta.m) + s.b[stream[i]];
      
      
      alpha[i] <- exp(lalpha[i]); 
      beta[i] <- exp(lbeta[i]);
      
    }

    for(SS in 1:Nstream){
      s.a[SS] ~ dnorm(0,pow(s.a.sd,-2));
      s.b[SS] ~ dnorm(0,pow(s.b.sd,-2));
    }


  }
}  
", 
file = here(modelScript.name))

My thought is that I would do something like this:

  for(i in 1:Nstream){
    yP[i] ~ dt(alpha[i] * SS[i] * exp(-1*beta[i]*SS[i]),XXX,YYY)
  }

But I don't understand what would go in place of XXX and YYY like they had in the linked example, or if using dt() is even appropriate.