Bayesian Methods
Priors and posteriors, MCMC and HMC, variational inference, Gaussian processes and model comparison.
7concepts
145flashcards
55minutes of reading
- 01 Hierarchical Models and Partial Pooling A hierarchical model lets groups borrow strength from each other by learning how different they are, which turns the choice between separate estimates and one pooled estimate into a parameter the data decides.
- 02 Posterior Predictive Checks and the Bayesian Workflow A posterior predictive check simulates replicated data from the fitted model and asks whether they look like the real data in the ways that matter, and it is one step in a workflow that also checks priors, computation and the model's ability to recover known parameters.
- 03 Priors, Conjugacy and the Posterior How a prior functions as pseudo-data, why conjugate families make the posterior a closed-form update, and why "uninformative" priors are informative on some scale.