Define, compile, & simulate the regression model

Upon observing the relationship between weight \(Y\)_i and height \(X\)_i for the 507 subjects \(i\) in the bdims data set, you can update your posterior model of this relationship. To build your posterior, you must combine your insights from the likelihood and priors:

likelihood: \(Y\)_i \(\sim N(m\)_i, \(s^2)\) where \(m\)_i \(= a + b X\)_i
priors: \(a \sim N(0, 200^2)\), \(b \sim N(1, 0.5^2)\) and \(s \sim Unif(0, 20)\)

In this series of exercises, you'll define, compile, and simulate your Bayesian regression posterior. The bdims data are in your work space.

DEFINE your Bayesian model.

Define the likelihood model of Y[i] given m[i] and s where m[i] <- a + b * X[i].
Specify the priors for a, b, and s.
Store the model string as weight_model.

Exercise

Define, compile, & simulate the regression model

Instructions 1/3