Bayesian Inference with R training course teaches delegates the Bayesian approach to inference using the R language as the applied tool. After a quick review of importing and managing data with R as well as base R commands, delegates will learn the theoretical underpinnings of inference (with a focus on Bayesian statistics), along with applied examples of Bayesian approaches to statistical models.
By attending Bayesian Inference with R workshop, delegates will learn to:
- Understand how to import data to R for use in statistical modeling
- Review the frequentist approach to making inference on populations, using samples of data
- Non-comprehensive review of probability theory
- Understand maximum likelihood and restricted maximum likelihood
- Contrast frequentist approaches to inference with Bayesian approaches to inference
- Understand how prior distributions affect posterior distributions
- Review the difference between proper and improper priors
- Understand how to implement and explain an MCMC algorithm for obtaining empirical prior distributions
- Fit Bayesian modeling approaches to the general linear modeling framework
- Account for clustering and repeated events over time using Bayesian inference (generalized linear models)
- Make inference on functions of parameters
- Properly interpret Bayesian posterior density intervals
- Develop awareness of different modern software approaches to making Bayesian inference (with a focus on R)
- Basic background in R programming including importing and manipulating data, and an understanding of base R data structures such as vectors, matrices, lists, and dataframes.
- Basic background in frequentist statistics to include hypothesis testing (p-values and null hypotheses), and statistical tests such as t-tests and chi-square tests. An understanding of the general linear modeling framework will be helpful.