SCHEDULE

This is the evolving schedule of topics -- there will be changes and updates as we progress through semester.

The main handouts for the course will be available here as Postscript (ps) or PDF (pdf) files to download (see the Computing page for info on Postscript viewers; Adobe Acrobat is standard for PDF files).

This material includes notes on various topics, many of them by my colleague Mike West, and copies of overhead projector slides (many of which will be) used in class. These are or will be available on the class schedule page below, and are required reading. You are most strongly advised to copy and read such material in advance of class: The handouts are critical: much of the class discussion will be covered in handouts. Click on the ps or pdf link by the class dates for copies of the slides, and on the Handouts items for copies of notes. References to relevant sections of the main text -- DeGroot -- and the support text -- Gelman et al. -- appear in the "Advance Reading" column too. Beyond this, a few items of additional material (e.g., example S-Plus code), are available from links on the Computing and Datasets pages.

In the Advanced Reading column DG refers to the book by DeGroot, Lee refers to the book Bayesian Statistics by Peter Lee, and GCSR refers to the (optional) book by Gelman, Carlin, Stern and Rubin. Here's a sheet of pdf's (ps, pdf) you might find useful.

CLASSDATETOPICSADVANCE READING
1Wed 12 Jan Hello and Introduction Handout 1 (ps, pdf)
DG 1.2,1.3 ; GCSR 1.1, 1.2 (+1.5) or Lee 1.1, 1.3, 1.5
- Mon 17 JanMLK Birthday: No Class -
2Wed 19 Jan
(ps, pdf) 		Probability and Inference: Introduction, Diagnosis example Handout 1 (ps, pdf)
DG 6.1; GCSR 1.4, 1.5 or Lee 1.2,1.4
3 Mon 24 Jan
(ps, pdf) 		Binomial models: Sampling model, likelihood function, relative likelihood, maximum likelihood estimates Handout 2 (ps, pdf)
Sec 1-3.2
DG 6.2, p340-341; GCSR 2.1, 1.3 or Lee 1.3, A.10, A.11
. Wed 26 Jan Snow Day; no class..
4 Mon 31 Jan More on likelihood functions and intervals; 3-4% rule, log-quadratic approximations; transformations Handout 2,
Sec 1,3.3,3.5; exs 6,7
5 Wed 2 Feb Normalised likelihoods, reference Bayesian analyses in binomial models Handout 2
Sec 3.6, 4.1, 4.2; Lee 3.1
6 Mon 7 Feb Beta distributions: using and understanding posterior estimates and intervals; preliminary testing ideas Handout 2
4.2, 4.3, 4.5
7 Wed 9 Feb Simulation of posteriors; comparing two binomial populations; relative risks
intro to sequential updating 		Handout 2
(end of Sec 4.7), 5.1, 4.5
8 Mon 14 Feb Prior-to-posterior updating; Conjugate beta priors; "uninformative" priors; large sample normal approximation to beta posterior Handout 2
Sec 4.5, 4.6, 4.4
DG p319, intro of Sec 6.3
9 Wed 16 Feb Exploring comparisons of several binomials;
multiple studies of relative risks;
prospective and case-control studies 		Handout 2
Sec 5.3, 5.2
10 Mon 21 Feb Sampling theory for proportions
Confidence intervals 		Handout 2, revised Sec 8 (ps,pdf); DG, bottom of p.399-top of p.400
11 Wed 23 Feb
(ps, pdf)		 Random Sampling Models in general
Likelihood functions, MLEs, Bayes, Sufficiency; Poisson data examples 		DG 6.2, esply p316,317,323
intros to DG 6.5, 6.6, 6.7
12 Mon 28 Feb
(ps, pdf) 		Introducing normal models
Likelihood and posteriors for normal means 		DG Theorem 3 of Sect 6.3
Example 2 on p341
Handouts 3 (ps, pdf) and 4 (ps, pdf)
13 Wed 1 Mar Inference on a normal mean: reference posteriors, confidence intervals, comparative inference issues Handouts 3 and 4 -- see above
14 Mon 6 Mar Pre-Exam Review Homeworks, Notes etc
15 Wed 8 Mar Midterm Exam (ps, pdf) Results (ps, pdf)
 2:20pm-3:35pm
- Mon 13 MarSpring Break: No Class-
- Wed 15 MarSpring Break: No Class-
16 Mon 20 Mar
(ps, pdf)		 Introduction to regression models DeGroot Chap 10,
pollute.ssc, merc.ssc
17 Wed 22 Mar Straight line models, least squares estimation see above and
Handout 5 (ps, pdf)
18 Mon 27 Mar Straight line models: Freq & Bayes inference see above
19 Wed 29 Mar Straight line models: model assessment see above
20 Mon 3 Apr
(ps, pdf)		 Introducing multiple regression models -
21 Wed 5 Apr QQ plots: normal quantile plots and residual analysis Handout 6 (ps, pdf) and
qqbayes.ssc S-Plus function
22 Mon 10 Apr More on multiple regression -
23 Wed 12 Apr Ideas of hypothesis testing
(Handout 7: Sections 1,2, 3.1, 3.2, and 4) 		Handout 7 (ps,pdf) and
* "Illusion of Objectivity" in-class handout, and
* DeGroot 8.1, 8.2
24 Mon 17 Apr Testing in Regression -
25 Wed 19 Apr More hypothesis testing -
26 Mon 24 Apr Review & Leftovers-
27 Wed 26 Apr Review & Practice Exam QuestionsPractice Exam (ps,pdf)
- Fri 5 MayFinal Exam (Practice: ps,pdf) 7pm-10pm