A Bayesian Perspective on Missing Data Imputation
This lecture discusses some approaches to handling missing data, primarily when missingness occurs completely randomly. We discuss a procedure, MICE, which uses Gibbs sampling to create multiple "copies" of filled-in datasets.
Bayesian Generalized Linear Models
This lecture discusses a simple logistic regression model for predicting a binary variable. GLMs are necessary when the response variable cannot be modeled appropriately by a normal distribution, and use a link function to connect parameters of the response distribution to the covariates.
Penalized Linear Regression and Model Selection
This lecture covers some Bayesian connections to penalized regression methods such as ridge regression and the LASSO. Further discussion of the posterior predictive distribution as well as model selection criterion (DIC) is included.
Bayesian Linear Regression
The main difference with traditional approaches is in the specification of prior distributions for the regression parameters, which relate covariates to a continuous response variable. However, the Bayesian approach also provides a fairly intuitive way to add random effects (such as a random intercept or random slope), which results in what is traditionally known as a linear mixed model.
This model is useful for accommodating data which are grouped or having multiple levels, as the main feature is the addition of a between-group layer which relates groups to each other. The presence of this layer forces group-level parameters to be more similar to each other, displaying the important properties of partial pooling and shrinkage.
This lecture discusses the Metropolis and Metropolis-Hastings algorithms, two more tools for sampling from the posterior distribution when we do not have it in closed form. These are used when we are unable to obtain full conditional distributions. MCMC for the win!
The Normal Model in a Two Parameter Setting
This lecture discusses Bayesian inference of the normal model, particularly the case where we are interested in joint posterior inference of the mean and variance simultaneously. We discuss approaches to prior specification, and introduce the Gibbs sampler as a way to generate posterior samples if full conditional distributions of the parameters are available in closed-form.
Bayesian Inference for the Normal Model
The normal distribution has two parameters, but we focus on the one-parameter setting in this lecture. We also introduce the posterior predictive check as a way to assess model fit, and briefly discuss the issue with improper prior distributions.
Monte Carlo Sampling
This lecture discusses Monte Carlo approximations of the posterior distribution and summaries from it. While this might not seem entirely useful now, this underlies some of the key computational methods used for Bayesian inference that we will discuss further.