title: The reason for using update() on a model created with jags
body: <p>I'm using rjags to do an analysis in R (based off of this blogpost: <a href="http://www.sumsar.net/blog/2013/08/bayesian-estimation-of-correlation/" rel="nofollow">http://www.sumsar.net/blog/2013/08/bayesian-estimation-of-correlation/</a>) and I've got a question. Is there any difference between using update() to update the model with 500 samples and just setting the n.adapt parameter in the original model creation to 1000 instead of 500?</p>
  
  <p>In other words: is there an important difference between run1 and run2 when I do the following:</p>
  
  <pre><code>require(mvtnorm)
  require(rjags)
  
  model_string &lt;- "
    model {
      for(i in 1:n) {
        x[i,1:2] ~ dmnorm(mu[], prec[ , ])
      }
  
      prec[1:2,1:2] &lt;- inverse(cov[,])
      cov[1,1] &lt;- sigma[1] * sigma[1]
      cov[1,2] &lt;- sigma[1] * sigma[2] * rho
      cov[2,1] &lt;- sigma[1] * sigma[2] * rho
      cov[2,2] &lt;- sigma[2] * sigma[2]
  
      sigma[1] ~ dunif(0, 1000) 
      sigma[2] ~ dunif(0, 1000)
      rho ~ dunif(-1, 1)
      mu[1] ~ dnorm(0, 0.001)
      mu[2] ~ dnorm(0, 0.001)
  
      x_rand ~ dmnorm(mu[], prec[ , ])
    }
  
  "
  
  mu &lt;- c(10, 30)
  sigma &lt;- c(20, 40)
  rho &lt;- -0.7
  cov_mat &lt;- rbind(c(     sigma[1]^2       , sigma[1]*sigma[2]*rho ),
                   c( sigma[1]*sigma[2]*rho,      sigma[2]^2       ))
  x &lt;- rmvnorm(30, mu, cov_mat)
  
  data_list = list(x = x, n = nrow(x))
  inits_list = list(mu = c(mean(x[, 1]), mean(x[, 2])),
                    rho = cor(x[, 1], x[, 2]),
                    sigma = c(sd(x[, 1]), sd(x[, 1])))
  
  
  jags_model &lt;- jags.model(textConnection(model_string), data = data_list, inits = inits_list,
                           n.adapt = 500, n.chains = 3, quiet = T)
  update(jags_model, 500)
  mcmc_samples &lt;- coda.samples(jags_model, c("mu", "rho", "sigma", "x_rand"),
                               n.iter = 5000)
  
  run1&lt;-summary(mcmc_samples)
  
  jags_model &lt;- jags.model(textConnection(model_string), data = data_list, inits = inits_list,
                           n.adapt = 1000, n.chains = 3, quiet = T)
  mcmc_samples &lt;- coda.samples(jags_model, c("mu", "rho", "sigma", "x_rand"),
                               n.iter = 5000)
  run2&lt;-summary(mcmc_samples)
  </code></pre>