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.

本练习是课程的一部分

Bayesian Modeling with RJAGS

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交互式实操练习

通过完成这段示例代码来试试这个练习。

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

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

编辑并运行代码