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This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from classical inferential statistics that prohibits probability statements about parameters and is based on asymptotically sampling infinite samples from a theoretical population and finding parameter values that maximize the likelihood function. Mostly notorious is null-hypothesis significance testing (NHST) based on p-values.
Features
- Hierarchical Models
- Bayesian Logistic Regression
- Bayesian Regression with Count Data: Poisson Regression
- Common Probability Distributions
- Markov Chain Monte Carlo (MCMC) and Model Metrics
- Model Comparison: Cross-Validation and Other Metrics
Project Samples
