
Copies of lecture
slides will usually appear here before each lecture.
I recommend that you bring copies of the slides to each lecture.
The 4up slides would be best for this purpose.
For some lectures there is R code that implements examples
illustrated in the lectures.
Lecture 
Date 
Topic 
1up
slides 
4p
slides 
R
code 
1 
10/10/16 
Exponential families 
lecture1.pdf 
lecture1_4p.pdf 

2 
11/10/16 
Sufficiency, Factorization Theorem, Minimal sufficiency 
lecture2.pdf 
lecture2_4p.pdf  
3 
17/10/16 
Estimators, Minimum Variance Unbiased Estimators (MVUE) The CramérRao Lower Bound. 
lecture3.pdf  lecture3_4p.pdf  
4 
18/10/16 
Consequences of the CramérRao Lower Bound. Searching for a MVUE. RaoBlackwell Theorem. 
lecture4.pdf  lecture4_4p.pdf  
5 
24/10/16 
LehmannScheffé Theorem and completeness. The
method of moments 
lecture5.pdf  lecture5_4p.pdf  
6 
25/10/16 
Bayesian Inference. 
lecture6.pdf  lecture6_4p.pdf  
7 
31/10/16 
Prior distributions. Predictive
distributions. Normal approximations. 
lecture7.pdf  lecture7_4p.pdf  
8 
01/11/16 
Hierarchical models 
lecture8.pdf  lecture8_4p.pdf  
9 
7/11/16 
Bayesian hypothesis testing.  lecture9.pdf  lecture9_4p.pdf  
10 
8/11/16 
Decision theory. Loss functions. Risk functions. Admissible and inadmissible rules. Minimax. Bayes Risk and Bayes rules. Randomised rules.  lecture10.pdf  lecture10_4p.pdf  
11 
14/11/16 
Finding minimax rules. Hypothesis testing with loss functions. 
lecture11.pdf  lecture11_4p.pdf  
12 
15/11/16 
Stein's paradox and the JamesStein estimator. 
lecture12.pdf  lecture12_4p.pdf  
13 
21/11/16 
Empirical Bayes. 
lecture13.pdf  lecture13_4p.pdf  
14 
22/11/16 
Variational Bayes. 
lecture14.pdf  lecture14_4p.pdf  
15 
28/11/16 
The EM algorithm (draft version) 
lecture15.pdf  lecture15_4p.pdf  
16 
29/11/16 
Bayesian
analysis of contingency tables. Bayesian linear
regression. 
lecture16.pdf  lecture16_4p.pdf  Bayes_table.R 
For UG students: There will be classes
in weeks 3,5,7 and 8 on Wed.(10am and 11:30 am, tutor J. berestycki) and in weeks 3,4,7 and 8 on Thur. (9:30am and 12 am, tutor S. Filippi)
For UG students only: Work should be handed in to the appropriate BS2 tray (by noon the Monday prior to the class).
Public 
Time 
Location 
Tutor 
Assistant 
Weeks 
U.G. 
Wed. 1011:30 am 
LG.04 
Julien Beresycki 
TBA 
3,5,7,8 
U.G. 
Wed. 11:30am1pm 
LG.04 
Julien Beresycki 
TBA 
3,5,7,8 
U.G. 
Thur. 9:3011:00 am 
LG.05 
Sarah Filippi 
TBA 
3,4,7,8 
U.G. 
Thur. 12am1:30pm 
LG.05 
Sarah Filippi 
TBA 
3,4,7,8 
msc 
Thur. 9am10am 
LG.01 
Julien Berestycki 
NA 
3,5,7,8 
Class 
Sheet 
Week 3 
Sheet1.pdf 
Week 4 
Sheet2.pdf Version for week 4 
Week 5 
Sheet2.pdf Version for week 5 
Week 7 
Sheet3.pdf 
Week 8 
Sheet4.pdf 