732A89 · 6 credits · Advanced level (A1N) · Statistik
Computational Statistics
Active · next start VT 2027
78%
passed the latest regular sitting
DAT2 · 23 Mar 2026 · 23 students
75%
passed the regular sittings, all sittings over the last 5 years
DAT2 · 44 students
78%
Harder than 78% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 44 students.
Part of 1 programme
Other module
DAT2 Examination
- A
- B
- C
- D
- E
- F
Main rounds
75% passed · 44 students
- 78%23
- 71%21
Later rounds
68% passed · 19 students
- 75%8
- 64%11
- Pass a later round: 68% (19 students), compared with 75% (44 students) on the ordinary exam
- Get B or A: 25% (63 students)
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 67%3
- 67%3
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB2 Laboratory work
- P
Main rounds
100% passed · 45 students
- 100%27
- 100%18
Later rounds
100% passed · 8 students
- 100%8
- Pass a later round: 100% (8 students), compared with 100% (45 students) on the ordinary exam
About the course
Aim
After completion of the course the student should be able to:
- account for how computer arithmetics affects statistical computations
- develop computer implementations of standard methods for simulating from complex statistical distributions
- develop computer implementations of commonly used deterministic and stochastic optimization methods in statistics and machine learning
- select a suitable computer intensive uncertainty estimation methods for a given problem, and develop an implementation of the algorithm in a programming language
- interpret the results obtained by various simulation and estimation methods
- plan and carry out course work within given time frames
Content
The course covers the following topics:
- effect of computer arithmetics on statistical computations
- basic methods for random number generation, including inverse CDF method and acceptance/rejection method
- Monte Carlo methods for simulation and inference, including bootstrap and jackknife
- Markov Chain Monte Carlo (MCMC) simulation, including Metropolis-Hastings and Gibbs samplers
- introduction to unconstrained deterministic and stochastic optimization methods
- Examiner
- Frank Miller
- Main field of study
- Statistik