Bayesian statistics
Graduate · Statistics
Syllabus focus
Theoretical / proof-based
Pricing
Graduate-level rates are set on consultation. See the pricing page for K–12 and undergraduate rates.
Topics typically covered
Theoretical / proof-based
Bayesian foundations
- Coherent inference and Dutch book arguments (intro)
- Prior construction: conjugate, Jeffreys, reference
- Posterior asymptotics: Bernstein–von Mises
- Bayes factors and model selection
- Decision theory: Bayes rules and admissibility
Computation
- Monte Carlo integration and importance sampling
- MCMC: Metropolis–Hastings, Gibbs, HMC (overview)
- Convergence diagnostics: R-hat, effective sample size
- Variational inference (introduction)
- Approximate Bayesian computation (ABC)
Hierarchical modeling
- Exchangeability and hierarchical priors
- Empirical Bayes and hyperpriors
- Spatial and spatiotemporal Bayes models (intro)
- Nonparametric Bayes: Dirichlet process (overview)
- Sensitivity analysis and robust priors
Notes
PhD-level Bayesian course covering computation and theory. Expect prior elicitation, MCMC convergence, and hierarchical modeling.