Define, compile, & simulate the Normal-Normal

Upon observing the change in reaction time \(Y\)_i for each of the 18 subjects \(i\) enrolled in the sleep study, you can update your posterior model of the effect of sleep deprivation on reaction time. This requires the combination of insight from the likelihood and prior models:

likelihood: \(Y\)_i \(\sim N(m, s^2)\)
priors: \(m \sim N(50, 25^2)\) and \(s \sim Unif(0, 200)\)

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

This exercise is part of the course

Bayesian Modeling with RJAGS

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Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# DEFINE the model    
___ <- "model{
    # Likelihood model for Y[i]
    for(i in 1:___) {
        Y[i] ~ ___
    }

    # Prior models for m and s
    m ~ ___
    s ~ ___
}"

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