Lessons
Lesson 0: Setting up computing resources
Lesson 1: Introduction to Bayesian modeling
Probability as the logic of science
Notation of parts of Bayes’s Theorem
Marginalization
Bayes’s theorem as a model for learning
Probability distributions
Tasks of Bayesian modeling
Bayesian modeling example: parameter estimation from repeated measurements
Choosing likelihoods
Choosing priors
Lesson 2: Parameter estimation by optimization
Lesson 3: Markov chain Monte Carlo and Stan
Lesson 4: Display of MCMC results and diagnostics
Lesson 5: Prior and posterior predictive checks
Lesson 6: Hierarchical models
Lesson 7: Principled pipelines
Exercises
Exercise 1. Practice with Bayesian modeling
Exercise 2. Paremeter estimation by optimization
Exercise 3. First foray into MCMC
Exercise 4: More Bayesian inference with MCMC
Exercise 5: Bayesian modeling with prior and posterior predictive checks
Exercise 6: Hierarchical models
Exercise 7: Principled pipelines
Schedule
Schedule overview
Daily schedule
PoL workshop on statistical inference, part 2
Lesson 1: Introduction to Bayesian modeling
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Lesson 1: Introduction to Bayesian modeling
Probability as the logic of science
Notation of parts of Bayes’s Theorem
Marginalization
Bayes’s theorem as a model for learning
Probability distributions
Tasks of Bayesian modeling
Bayesian modeling example: parameter estimation from repeated measurements
Choosing likelihoods
Choosing priors