Code

Data

• NFL field goal data (data)

Introduction

• Bayesian modeling of a disease outbreak (Background, NYT article about COVID-19 and Bayes)

Selecting priors

• Bayesian learning GUIs
• List of conjugate priors
• Bayesian analysis of a covariance matrix

Bayesian theory

• Bayesian Central Limit Theorem
• Posterior consistency
• Simulation study

Bayesian computation

• Gibbs – toy example
• Gibbs – simple linear regression
• Gibbs – multiple linear regression
• Slice sampling
• Step-by-step illustration of Metropolis sampling
• Visualization of various MCMC algorithms
• Metropolis – logistic regression
• Metropolis – extreme value analysis with MCMC diagnostics
• Block Metropolis – logistic regression
• Metropolis-Hastings – geostatistics
• HMC- logistic regression
• Simple linear regression in JAGS
• Poisson-gamma model in JAGS
• Example of poor convergence
• Example of good convergence
• ABC – beta/binomial
• ABC – SIR

Handling large datasets

• Variational Bayes – linear regression
• Variational Bayes – logistic regression
• ADAM
• SGMCMC – logistic regression

Hierarchical models

• Bayesian LASSO
• Prior predictive checks for logistic regression
• Probit regression
• Random effects logistic regression
• Disease modeling
• Bayesian inverse modeling
• Missing and censored data

Model fit

• Cross validation
• Bayesian multiple testing
• Bayesian R^2
• DIC/WAIC
• Posterior predictive checks
• Posterior predictive checks for censored data

Machine learning

• Stochastic search variable selection
• Regression with horseshoe priors
• Generalized additive model
• Bayesian feed-forward neural network (gradient derivation and numerical confirmation)
• Prior for a PDF
• Estimating a PDF with a finite mixture of normals
• Regression with mixture of normal residuals

Additional data and example are available here.