Standard syllabus
Bayesian statistics · Undergraduate · Math
Learning objectives from the Bayesian statistics syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Bayesian statistics — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Bayesian foundations
- Subjective probability — Subjective probability
- Bayes' theorem — Bayes' theorem
- Prior, likelihood, and posterior — Prior, likelihood, and posterior
- Conjugate priors: beta-binomial, normal-normal — Conjugate priors: beta-binomial, normal-normal
- Credible intervals vs confidence intervals — Credible intervals vs confidence intervals
- Bayesian hypothesis testing (introduction) — Bayesian hypothesis testing (introduction)
Computation and models
- Posterior simulation: Monte Carlo methods — Posterior simulation: Monte Carlo methods
- Introduction to MCMC: Metropolis–Hastings and Gibbs — Introduction to MCMC: Metropolis–Hastings and Gibbs
- Bayesian linear — Bayesian linear
- Logistic regression — Logistic regression
- Model comparison: Bayes factors (intro) — Model comparison: Bayes factors (intro)
- Sensitivity to prior choice — Sensitivity to prior choice
Applications
- Hierarchical models (introduction) — Hierarchical models (introduction)
- Empirical Bayes methods — Empirical Bayes methods
- Bayesian model averaging (overview) — Bayesian model averaging (overview)
- Communicating posterior uncertainty — Communicating posterior uncertainty
Learning objectives
Click an objective for study materials.
Bayesian foundations
- Subjective probability — Subjective probability
- Bayes' theorem — Bayes' theorem
- Prior, likelihood, and posterior — Prior, likelihood, and posterior
- Conjugate priors: beta-binomial, normal-normal — Conjugate priors: beta-binomial, normal-normal
- Credible intervals vs confidence intervals — Credible intervals vs confidence intervals
- Bayesian hypothesis testing (introduction) — Bayesian hypothesis testing (introduction)
Computation and models
- Posterior simulation: Monte Carlo methods — Posterior simulation: Monte Carlo methods
- Introduction to MCMC: Metropolis–Hastings and Gibbs — Introduction to MCMC: Metropolis–Hastings and Gibbs
- Bayesian linear — Bayesian linear
- Logistic regression — Logistic regression
- Model comparison: Bayes factors (intro) — Model comparison: Bayes factors (intro)
- Sensitivity to prior choice — Sensitivity to prior choice
Applications
- Hierarchical models (introduction) — Hierarchical models (introduction)
- Empirical Bayes methods — Empirical Bayes methods
- Bayesian model averaging (overview) — Bayesian model averaging (overview)
- Communicating posterior uncertainty — Communicating posterior uncertainty
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Bayesian foundations
Subjective probability
Coming soon - Computation and models
Posterior simulation: Monte Carlo methods
Coming soon - Applications
Hierarchical models (introduction)
Coming soon
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$1,162 · Bayesian statistics · 18 tutoring hrs
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