Conclusion
1. Conclusion
The popularity of Bayesian modeling has grown along with the availability of computing resources required for its application. This course explored one of these resources: RJAGS.2. Bayesian modeling with RJAGS
Within RJAGS, you learned how to define, compile, & simulate intractable Bayesian models - that is, models that might otherwise be out of reach. You also explored the Markov chain mechanics behind RJAGS simulation.3. The power of Bayesian modeling
Along the way, you absorbed the power of Bayesian modeling. You utilized the formal Bayesian framework to combine insights from your data *and* priors to inform your posterior insights.4. The power of Bayesian modeling
Further, you learned how to conduct intuitive posterior inference, including the construction of posterior credible intervals and probabilities.5. Foundational, flexible, & generalizable Bayesian models
You engineered a set of foundational, flexible, and generalizable Bayesian models. Starting from the simple Normal-Normal model for variable $Y$,6. Foundational, flexible, & generalizable Bayesian models
you constructed a simple Normal Bayesian regression model of $Y$ by a single quantitative predictor variable,7. Foundational, flexible, & generalizable Bayesian models
and a single categorical predictor variable.8. Foundational, flexible, & generalizable Bayesian models
You extended these methods to Normal multivariate regression with more than one predictor variable and, ultimately,9. Foundational, flexible, & generalizable Bayesian models
generalized this to Poisson multivariate regression models for count variables.10. Thank you!
Thanks for following along. I hope you continue to explore the power and flexibility of Bayesian modeling in RJAGS!Create Your Free Account
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