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732A90 Computational statistics

Course information

This is the coursepage for the course in Computational Statistics.

The first course occasion was a lecture in room U2 on Tuesday 2023-10-31 10:15-12.

This course is an obligatory course in the master's program Statistics and Machine Learning. Other students can apply for it as a single-subject course.

The target student group are those who are familiar with statistics, have good mathematical background and practical experience of programming in some computer language.

The programming language of the course is R.

The course literature is:

Course structure

The course contains 8 lectures, 6 computer labs, and 3 seminars. Attendance at the seminars is mandatory.

Lectures, computer labs, and seminars will be on campus; see TimeEdit for dates, times, and locations.

Six topics (lecture, lab, and seminar hour) will focus on different aspects of computational statistics and students will then work in groups of two and two. Two lectures (LM1-LM2) will focus on a recall of basic mathematics required for the course.

The best thing is that the students work with their own computer.

The course contains three teaching activities:

  • Lecture (Fö) Introduction of new concepts.

  • The following content will be presented in the lectures.

    Topic 1: Unidimensional optimization and computer arithmetics.

    Read: Gen, Ch. 2, 6.1; GH, Ch. 2.1
    Single subject course students: Self study of R syntax and basic functions, read and test the code from here

    Topic 2: Multidimensional optimization.

    Read: Gen, Chapter 6.1 and 6.2; GH, Chapter 2.2

    Topic 3: Random number generation.

    Read: Gen, Chapter 7.1-7.3; GH, Chapter 6

    Topic 4: Monte Carlo methods, MCMC.

    Read: Gen, ch. 7.3-7.4; GH, Chapter 7

    Topic 5: Numerical model selection and hypothesis testing

    Read: Gen, Chapter 12, 13 and handouts. Those of you who are not familiar with hypothesis testing, read Chapter 6 in Computational Statistics Handbook with Matlab by Martinez

    Topic 6: EM algorithm and stochastic optimization

    Read: Gen, Chapter 6.3, "Pattern Recognition" by Bishop, Chapter 9.2-9.3, paper "Large-scale machine learning with stochastic gradient descent" by Bottou (2010)

    Lecture Date and time Material
    L1 2023-10-31 10:15 Lecture 1
    R code for Newton
    Bisection video
    LM1 2023-11-06 08:15 Analytic optimization
    Basic matrix algebra
    Some R code for this lecture
    L2 2023-11-07 10:15 Lecture 2
    R code for steepest ascent example
    LM2 2023-11-14 10:15 Lecture notes LM2
    L3 2023-11-15 13:15 Lecture 3
    L4 2023-11-21 10:15 Lecture 4
    R code Metropolis algorithm (Example 1 from lecture)
    R code for ellipse (Gibbs example)
    L5 2023-11-28 10:15 Lecture 5 PDF
    ZIP (with R code)
    L6 2023-12-05 10:15 Lecture 6 PDF
    ZIP (with R code)

  • Computer lab (DATALAB) Individual computer lab with individual help.
  • Attendance of the lab sessions is not obligatory but it might be difficult to complete the labs without supervision so it is recommended to attend these sessions.
    Students must discuss their lab solutions in a group (groups will be setup on the first Lab) and compile a collaborative report showing the results and the code. If the group is presenting then it should be done in such a way that the report document can directly be used for a presentation at the seminar (but code is still to be provided). Attention: there is a deadline for such a report! The document should clearly state the names of the students that participated in its compilation. This report should be submitted via LISAM as a .PDF (alternatively in case of problems emailed to one of the responsible staff ) before the report deadline.
    ALL will be CHECKED through URKUND for plagarism (also with respect to past labs)!
    The file should be named Group X.pdf where X is the group number. Please also include your names in the report.
    The collaborative reports are corrected and graded by the teacher. A student is PASSED on the lab if the group report is PASSED.

    All group members have to contribute to, understand and be able to explain all aspects of the work. In case some member(s) of a group do not contribute equally this has to be reported and in this situation a formal group work contract will be signed, stipulating the consequences for further unequal contributions.

    If you miss the deadline for a lab solution, you must submit the solution anyway, and in this case an additional assignment will also be given.
    There is a second deadline of 23:59 28 January 2024 for submitting corrections for all the hand-ins (note: the previously announced deadline Jan 21 was extended).
    There is a final deadline of 23:59 18 February 2024 for all the hand-ins (note: the previously announced deadline Feb 11 was extended). After this date NO submissions nor corrections will be accepted.
    Assignment no. Instructions Lecture Lab Deadline Seminar Presenting groups Opposing groups
    1 Lab1.pdf 2023-10-31 2023-11-01 2023-11-07 2023-11-22 5, 9 8
    2 Lab2.pdf 2023-11-07 2023-11-08 2023-11-14 2023-11-22 3, 10 2
    3 Lab3.pdf 2023-11-15 2023-11-17 2023-11-22 2023-11-29 1, 14 15
    4 Lab4.pdf 2023-11-21 2023-11-24 2023-11-28 2023-11-29 13, 6
    5 Lab 5 PDF
    ZIP (with data files)
    2023-11-28 2023-12-01 2023-12-05 2023-12-12 7 4
    6 Lab 6 PDF
    ZIP (with data files)
    2023-12-05 2023-12-06 2023-12-22 2023-12-12 11, 12

  • Seminar (SE) Each student group will present/oppose their labs and we will discuss eventual problems.

  • Attendance of the seminars is mandatorry; each seminar involves a discussion of the latest labs. Each group will be assigned to either present their lab report or being opponents at some seminar (see table above for the group numbers). Each member of the opponent group should prepare at least two questions, comments, improvements, or suggestions to the speaker's group report. It is important that the opponents submit their own group report for the same lab within the deadlines as they will be provided with the group report(s) of other groups prior to the seminar. In Labs 4 and 6, no opposition will take place. In Lab 6, the presenting groups present as usual, but a solution can be incomplete.
    The presenting group is recommended to provide their presentation as a .PDF. Otherwise the group has to arrange for their own laptop for presenting at the Seminar.
    An inappropriately done presentation or opposition can result in an additional assignment or even lab failure for the appropriate group!
    Please contact the examiner in advance if you are speaker or opponent and cannot come to the seminar you are responsible for.
    If you miss a seminar, you need to submit a completely correct solution of the lab(s) discussed at this seminar, and an additional assignment will be given.
    If you miss two or more seminars, the laboratory part is graded FAILED and you are forwarded to attend the same series of the seminars in the next year course.


The students are suggested to work using their own computers. For this course the following software is needed. Everything is open source and free.

Information on how to install R and R-Studio: - Windows, - Mac - Linux/Ubuntu

Computer exam

The exam was a computer exam on 2024-01-09. Re-exams will be on 2024-03-19 and on 2024-05-13, between 8:00 and 13:00 in both cases.

Exam is akin to labs, the only difference is that you are given certain amount of hours and that you are not allowed to communicate with others in any way. The material that you are allowed to use at the exam: course books and a PDF document containing max 100 pages of own notes, text and figures (all content of the document should be well visible at 100% scale). This pdf will be submitted through LISAM; please read the instructions in LISAM carefully (e.g. zip-format, file name instructions). Note that you have to submit the document for each exam; even if you have submitted a document for a previous exam, you need to submit it or a new one again. The deadline for submitting this 100-page document is around 11 calendar days before the exam, see Lisam. This document will be available then for you during the exam.
Students who have participated in last year's course (fall 2022) but have signed up to this exam need to write me (frank.miller at liu) an email. I will add you then to this year's LISAM that you can submit the 100-page doc.

The material that will be included with the exam can be downloaded here.

Previous exams can be found here.


  • Krzysztof Bartoszek, lecturer L5-L6
  • Bayu Brahmantio, Héctor Rodriguez Déniz, teaching assistants
  • Frank Miller, examiner and lecturer L1-L4, LM1-LM2

Page responsible: Frank Miller
Last updated: 2024-01-30