R Tutorial: The posterior model

Want to learn more? Take the full course at https://learn.datacamp.com/courses/bayesian-modeling-with-rjags at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- You now have the pieces in place to construct a posterior model of p, your underlying support in the upcoming election. First, the Beta(45,55) prior model suggested that your support hovered around 45%. Subsequently, you polled n = 10 voters and recorded the number X that supports you. Conditioned on your support p, the likelihood model of X is Binomial. Upon observing X = 6, the corresponding likelihood function indicates that values of support p near 0.6 are the most compatible with your poll. The prior and likelihood, scaled here for comparison, don't completely agree. Yet both are valuable to Bayesian analysis: the prior contributes knowledge that you built prior to the most recent poll. The likelihood provides insight into the values of p that are most compatible with the current polling data. The posterior model combines the insights from the prior and likelihood. Here, the posterior reflects increased optimism about your election chances in light of the small but optimistic polling data. In the previous course, you learned that the exact specification of the posterior can be obtained through Bayes' Rule. Specifically, the posterior is proportional to the product of the likelihood and prior. However, in more sophisticated model settings, tidy, closed-form solutions to this formula might not exist. Thus in this course, we'll focus on approximating posterior models using RJAGS. RJAGS combines the power of R with the "Just Another Gibbs Sampler" or JAGS engine. To get started, first download the JAGS program outside R. Then within R, install the most recent version of the rjags package. There are three essential steps to all RJAGS analyses: define, compile, and simulate. To begin, we define the Bayesian model by a model string and store