Part A Simulation and Statistical Programming HT 2019

Statistical programming

Part A Simulation and Statistical Programming has two webpages, one webpage for the Simulation theme (lectured by Prof Berestycki) and this webpage for the Statistical Programming theme (which I lecture).   Prof Berestycki’s page is the ‘main’ page. Material common to both themes will appear there (for example, the problem sheets).

Lectures and practical sessions

The following lectures and practical sessions are based on material provided by Prof Evans.

 

We meet six times, Tuesdays 3-5pm in weeks 1,2 and 3 and Friday 9-11am in weeks 5,6 and 8. These meetings will start with a short lecture-style intro to the material, and then we move onto the computers and do some practical exercises.

Week 1 Slides & Practical 1 with sample solutions

Week 2 Slides & Practical 2 with sample solutions and lecture code

Week 3 Slides & Practical 3 with sample solutions and lecture code

Week 5 Slides & Practical 4 with sample solutions and lecture code

Week 6 Slides & Practical 5 with sample solutions

Week 8 Slides & Practical 6 (MHcode.R)

My old lecture notes and practical material from 2015 are available here.

Datasets

Cystic Fibrosis (cystfibr.txt)

Tetrahymena Data (hellung.txt)

Japanese beetle larvae data (beetlelarva.txt)

Speed Data (speed.txt)

Air Pollution Data (airpol.txt)

Image Data (noisy, true)

Resources

We will be using the statistical software package R, which you can get here. Please install it on your own computer and practice using it as early as possible. You may find it helpful to bring a laptop to use during lectures, but it is not necessary.

The software RStudio is also useful.

Here is a R-tutorial I prepared with questions and answers. It is a quick reminder of some core R.

Recommended reading

W.J. Braun and D Murdoch, "A First Course in Statistical Programming with R", ISBN 0-521-69424-8

C.P. Robert and G Casella, ``Introducing Monte Carlo Methods with R'

 

Advanced texts

W. Venables and B.D. Ripley, “Modern Applied Statistics with S”, ISBN 0- 387-95457-0

 

 

"Anyone who can do solid statistical programming will never miss a meal."

(Prof David Banks 2008)

 

Geoff Nicholls / nicholls@stats.ox.ac.uk