Course schedule Bayesian Statistics (BSc) 2021

Week 1 Frequentist statistics, introduction Bayesian statistics 
HC: sections 1.1, 1.2, 1.3, 1.4
WC: exercises 1.1, 1.2, 1.3, 1.4
HW: read chapter 1, appendices B.1, B.2, B.3, B.5

Week 2 Prior, posterior & model distributions, Bayes's Rule
HC: subsection 2.1.1, appendix B.5
WC: exercises 2.2, 2.3, 2.4
HW: read subsection 2.1.1

Week 3 Bayes's billiard, Bayesian view of the model, frequentist view of the posterior
HC: subsections 2.1.2, 2.1.3, 2.1.4, 2.1.5
WC: exercises 2.1, 2.5, 2.7
HW: read subsections 2.1.2, 2.1.3, 2.1.4, 2.1.5

Week 4 Bayesian point estimators
HC: section 2.2
WC: exercises 2.9, 2.10, 2.12
HW: read section 2.2

Week 5 Confidence sets and credible sets
HC: section 2.3
WC: exercises 2.14, 2.15, 2.13
HW: read section 2.3

Week 6 Tests and Bayes factors
HC: subsection 2.4
WC: exercises 2.16, 2.17
HW: read section 2.4

Week 7 Bayesian and frequentist decision theory
HC: section 2.5
WC: exercises 2.18, 2.19, 2.20
HW: read section 2.5

Week 8 Mid-term exam

Week 9 Subjective priors, non-informative priors
HC: sections 3.1, 3.2
WC: discuss mid term exam
HW: read sections 3.1, 3.2

Week 10 Hierarchical priors, empirical priors 
HC: section 3.3, subsection 3.4.1
WC: exercises 3.1, 3.2
HW: read sections 3.3, 3.4

Week 11 Empirical priors and estimation bias; conjugate and Dirichlet priors 
HC: subsection 3.4.2, sections 3.5, 3.6
WC: exercises 3.3, 3.4 (a.), (c.) and (e.)
HW: read sections 3.5, 3.6

Week 12 Dirichlet process priors and posteriors
HC: section 6.4, appendix D.1, D.2
WC: exercises 3.6, 3.7 
HW: read section 3.6, appendix D

Week 13 The Bernstein-von Mises theorem
HC: sections 4.1, 4.2
WC: exercises 4.2, 4.3, 4.5
HW: read sections 4.1, 4.2