R Tutorial: The prior 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. --- Hi! Welcome to "Bayesian Modeling with RJAGS." I'm Alicia Johnson. I'm an associate professor of Statistics at Macalester College and will be the instructor of this course. I assume that you've worked through the previous course in Bayesian Data Analysis, thus are familiar with the fundamental ideas behind Bayesian analysis and inference. In this course, you'll generalize these logical, flexible, and intuitive fundamentals to more advanced Bayesian model settings. Specifically, you'll explore foundational Bayesian models, such as the Beta-Binomial, Normal-Normal, and Bayesian regression models, that are easily generalized to broader settings. You will learn how to define, compile, and simulate these models using the RJAGS package in R. Finally, you will learn how to use RJAGS simulation output to conduct Bayesian posterior inference. Let's start with a review. Suppose you're running in an election for public office. Older polls suggest that you have the support of 45% of the voters. However, due to polling errors and fluctuations in support, this figure is uncertain. Engineered from past polling & election data, the prior probability model shown here captures this uncertainty: you'll most likely receive around 45% of the vote. It’s also unlikely, though possible, that you’ll receive as little as 30% or as great as 60% of the vote. To gain better insight, your campaign conducts a small poll of 10 voters. Among them, 6 (or 60%) plan to vote for you. The posterior model combines insights from the prior and these small polling data. Mainly, in light of the poll, the updated or posterior model of your election support is slightly more optimistic than the prior model. You continue to collect data. In a new poll, 48 of 90 polled voters (or 53