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|Lecturer||Jonathan Rougier, email@example.com|
|Level||H/6 and M/7, 10cp|
|Weeks||TB2, TW13 to TW18 (23 Jan to 3 Mar)|
|Official unit page||level H/6, level M/7|
|Timetable||1300-1350 Thu, MATH SM3|
|1700-1750 Thu, MATH SM2|
|1400-1450 Fri, MATH SM4|
|All lectures start promptly on the hour and last for fifty minutes. Modifications to the timetable appear below, under announcements.|
|Office Hour||1300-1350 Fri, MATH PC2 (Portacabin 2).|
|Please come at the start of the hour. One or two people cannot make this time: please talk to me about alternative arrangements.|
Navigation: Course information, details, homework.
Click on 'details' to see a lecture-by-lecture summary.
Final office hour.Tue 30 May, 10am, SM2.
Office Hours. Mon 8 May, 12 noon, PC2. Tue 16 May, 4pm, SM2.
Feedback available on Homework 2.
Summer Intern opportunity: http://www.uhbristol.nhs.uk/research-innovation/our-research/bristol-nutrition-bru/vacancies/.
23 Feb: Lectures at 1000 (SM4) and 1300 (SM3), no lecture at 1700.
You can buy this book from wordery.co.uk. Lectures will be supplemented with additional readings from this book. There is a webpage for this book.
You should also consult
Additionally, you will find it helpful to dip into
You can buy this book from wordery.co.uk. There is a webpage for this book. Andrew Gelman has a well-known and interesting blog.
For statistical theory, at the appropriate level of difficulty, see
When you are ready to produce beautiful visualisations of your data and your results, you will need
I don't currently use ggplot2 myself, but I am a dinosaur; hoping to upgrade to a mammal in due course.
Finally, there will be handouts to cover the more technical material in the second part of the course. The homeworks will contain code snippets which you can adapt.
We will run JAGS from within R. Make sure your version of R is up-to-date, e.g. by visiting CRAN. To run JAGS from within R you will need to install (from inside R) the rjags package, either using the GUI or by using the install.packages function. Here is the rjags reference manual.
If you prefer, you can use the computers in room G9 of the Maths Dept main building. Login and select 'All Programs/R/R x64 3.2.3'. JAGS and rjags are already installed.
You should brush up on your basic data wrangling skills in R. Read this excellent paper by Hadley Wickham. I find the dplyr package in R very useful.
Answers to previous exam papers will not be made available. The exam is designed to assess whether you have attended the lectures, read and thought about your lecture notes and the handouts, done the homework, and read a bit more widely in the textbooks. Diligent students who have done the above will gain no relative benefit from studying the answers to previous exam questions. On the other hand, less diligent students may suffer the illusion that they will do well in the exam, when probably they will not.
Instead, I will supply 'exam-style' questions in the homeworks for revision purposes.
Finally, please note that in the exam ALL questions will be used for assessment. The number of marks will not necessarily be the same for each question.
Here is a summary of the course, looking as far ahead as seems prudent. This plan is subject to revision. There will be some time at the end for revision of the major themes.
For background, have a read through Statistics: Another short introduction during your first couple of weeks.
Handout: Statistical modelling. Reading from the BUGS book: Preface, chs 1 and 3. If you are struggling to follow the handout or the reading, have a look at chs 1-4 of Hoff (2009).
Further reading: Sec 2.7. Make sure you understand the difference between a DAG and a CIG, and understand how the Moralization Theorem converts a DAG into a CIG.
Handout: Markov Chain Monte Carlo (some material still to come in section 3.5). Reading from the BUGS book: Chapter 4, sections 1 and 2. You might also find sections 10.4 and 10.5 of Hoff (2009) helpful. My handout is more rigorous than either of these books.
Weekend reading: complete section 3.2 by going through the proof of Theorem 3.4. You need to know this proof but it is not examinable.
Reading: BUGS Book, sections 4.3, 4.4, and 4.5. There are more handouts on the way … Here's one: practical issues, the end of the MCMC handout.
JAGS for rats handout. Here is the data: Rats.csv, Rats.xls, Rats.xlsx.
You are strongly encouraged to do the homeworks and to hand in your efforts, to be commented on and marked.
Here are the answers. (Minor update Fri 10 Feb).
Feedback. Q4 and Q5 were poorly done, with many answers showing lack of basic mathematical technique. By this stage, all students should know how to prove an equivalence, and how to inspect a proof to see that it is sound. The homework handed in is not the scratch-pad, where the proof is worked out, but the shop window where the proof is displayed. The same is true in an exam.
Basic mathematical technique (not just for exams, these are also used by professional mathematicians).
Here are the answers. (Minor update Fri 17 Feb).
Feedback.There are tips on drawing DAGs in the answer to Q2a. In Q2c, remember that a DAG can only tell you about the conditional independence of Xi and earlier X's in the ordering. So the only one that is inferrable from the DAG is (iii).
We covered Q4 in the Office Hour. We'll do another example in a homework or revision sheet.
Here are the answers. (Minor update Fri 24 Feb).
Feedback. Not really a large enough sample to provide feedback.
Here are the answers.
Feedback. Not really a large enough sample to provide feedback, but no one attempted Q3 and Q4 and these are really important for your understanding of what goes on 'under the hood' of a Gibbs sampler.
Here are the answers.