Recall the likelihood of the Bayesian regression model of weight \(Y\) by height \(X\): \(Y \sim N(m, s^2)\) where \(m = a + b X\). A 100,000 iteration RJAGS simulation of the posterior, weight_sim_big, is in your workspace along with a data frame of the Markov chain output:

> head(weight_chains, 2)
          a        b        s iter
1 -113.9029 1.072505 8.772007    1
2 -115.0644 1.077914 8.986393    2

The posterior means of the intercept & slope parameters, \(a\) & \(b\), reflect the posterior mean trend in the relationship between weight & height. In contrast, the full posteriors of \(a\) & \(b\) reflect the range of plausible parameters, thus posterior uncertainty in the trend. You will examine the trend and uncertainty in this trend below. The bdims data are in your workspace.

Posterior prediction

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