HT 2019 - Computational Statistics SB1.2/SM2
Lecturers: Prof Geoff Nicholls (lectures 1-7), Prof François Caron (lectures 8-13)
Class tutors: Prof François Caron, Giuseppe di Benedetto, Fadhel Ayed
Teaching Assistants: Giuseppe di Benedetto, Fadhel Ayed, Jingyue Lu
Practical demonstrator: Dominic Richards
will find on this webpage the material for the second half of the
course. The material regarding the first part is available from Geoff Nicholls' webpage.
Lectures, classes and practicals take place in the department of statistics, 24-29 Saint-Giles'.
|Practicals Undergrad |
|Problem classes MSc|
|Problem classes Undergrad|
Friday 10-11, 11-12, 12-13
There are two assessed practicals for undergraduate students in week 4 and 8. The student submission deadlines are:
are two non-assessed practicals for MSc students in week 3 and week 6,
and one week-long assessed practical in week 1 TT. The student
submission deadline for this assessed practical is Monday 10:00 TT week 2.
hand in the solutions to the problem sheets by Tuesday 12:00
class, at the
department of statistics, 24-29 Saint-Giles' (write your name and the
TA's name on the script), and send the R code by
email, in a single well-commented R-script to Giuseppe Di
<firstname.lastname@example.org> (Friday 10am class), Jingyue
Lu <email@example.com> (Thursday 16:45 and Friday 11
classes) or Fadhel Ayed <firstname.lastname@example.org> (Friday 12:00
class). Class allocation details are on Minerva (accessible from Oxford University network).
is no marking of problem sheets for MSc students. Please complete the
problem sheets before the class. Solutions will be available on
Specimen questions on Hidden Markov models and revisions
Specimen questions on Hidden Markov models
class for undergraduate students: Tuesday 7 May (week 2) and 21 May (week 4), 11-12am, LG01. We will cover the
above specimen questions on HMM + 2015 past paper Q5(a)(i-iii) + 2016 past paper Q5(a) + 2017 past paper Q3. MSc students are welcome to attend the class.
Please see Geoff Nicholls webpage for information on the revision class and consultation session on the first part of the course.
L. Wasserman. All of Statistics. A concise course in Statistical Inference. Chapter 8. Sptringer, 2010.
L. Wasserman. All of Nonparametric Statistics, Springer, 2005.
B. Efron, R.J. Tibshirani. An Introduction to the Bootstrap. Chapman and Hall, 1993.
B. Efron. Bootstrap methods: Another Look at the Jackknife. Annals of Statistics, Vol. 7(1), pp. 1-26, 1979. [pdf]
K.P. Murphy. Machine Learning. A probabilistic perspective. Chapters 17 and 18. The MIT Press, 2012.
D. Barber. Bayesian reasoning and machine learning. Cambridge University Press, 2012.
update: 30 April 2019