Bayesian Workflow

Andrew Gelman is a professor of statistics and political science at Columbia UniversityAki Vehtari is a professor of computer science at Aalto UniversityRichard McElreath is the director of the Max Planck Institute for Evolutionary AnthropologyDaniel Simpson is a machine learning engineer at dottxtCharles Margossian is an assistant professor of statistics at the University of British ColumbiaYuling Yao is an assistant professor of statistics at the University of TexasLauren Kennedy is a senior lecturer in mathematical science at the University of AdelaideJonah Gabry is an applied statistics researcher at Columbia UniversityPaul-Christian Bürkner is a professor of statistics at TU Dortmund UniversityMartin Modrák is a researcher in bioinformatics at Charles UniversityVianey Leos Barajas is an assistant professor of statistical sciences at the University of Toronto

• Part 1: From Bayesian inference to Bayesian workflow. 1 Bayesian theory and Bayesian practice. 2 Statistical modeling and workflow. 3 Computational tools. 4 Introduction to workflow: Modeling performance on a multiple choice exam. Part 2: Statistical workflow. 5 Building statistical models. 6 Using simulations to capture uncertainty. 7 Prediction, generalization, and causal inference/ 8 Visualizing and checking fitted models. 9 Comparing and improving models. 10 Statistical inference and scientific inference. Part 3: Computational workflow. 11 Fitting statistical models. 12 Diagnosing and fixing problems with fitting. 13 Approximate algorithms and approximate models. 14 Simulation-based calibration checking. 15 Statistical modeling as software development. Part 4: Case studies. 16 Coding a series of models: Simulated data of movie ratings. 17 Prior specification for regression models: Reanalysis of a sleep study.18 Predictive model checking and comparison: Clinical trial. 19 Building up to a hierarchical model: Coronavirus testing. 20 Using a fitted model for decision analysis: Mixture model for time series competition. 21 Posterior predictive checking: Stochastic learning in dogs. 22 Incremental development and testing: Black cat adoptions. 23 Debugging a model: World Cup football. 24 Leave-one-out cross validation model checking and comparison: Roaches. 25 Model building and expansion: Golf putting. 26 Model building with latent variables: Markov models for animal movement. 27 Model building: Time-series decomposition for birthdays. 28 Models for regression coefficients and variable selection: Student grades. 29 Funnel problem with latent variables: No vehicles in the park. 30 Computational challenge of multimodality: Differential equation for planetary motion. 31 Simulation-based calibration checking in model development workflow.