Monte Carlo and Empirical Methods for Stochastic Inference

Monte Carlo Methods and Empirical Methods for Stochastic Inference

FMSN50 / MASM11,    7,5 credits, A (Second Cycle)

General Information

Elective for: BME4, D4, E4-ae, F4, F4-bm, F4-fm, I4-fir, Pi4-bs, Pi4-fm, Pi4-bam, R4, Master Mathematical Statistics
Language of instruction: The course will be given in English

 

Contents

Simulation based methods of integration and statistical analysis. Monte Carlo methods for sequential problems. Markov chain methods, e.g. Gibbs sampling and the Metropolis-Hastings algorithm, for simulation and inference. Bayesian modelling and inference. The re-sampling principle, both non-parametric and parametric. Methods for constructing confidence intervals using re-sampling. Simulation based tests as an alternative to asymptotic parametric tests.

Examination details

LTH:

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written and oral project presentation. The final grade is given by a summary of the results of the different parts of the examination.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Parts:
Code: 0117. Name: Project Part 1.
Credits: 2,5. Grading scale: UG. Assessment: Written report on the first project
Code: 0217. Name: Project Part 2.
Credits: 5. Grading scale: UG. Assessment: Written and oral report on the rest of the projects

NF:

Parts

0703 Project part 1, 2,5 hp
Grading scale: Fail, Pass
0704 Project part 2, 5,0 hp
Grading scale: Fail, Pass

Admission

Admission requirements LTH:

Assumed prior knowledge: Programming experience. FMSF05 Links to an external site. Probability theory helps.
The number of participants is limited to: No
The course overlaps following course/s: FMS091 Links to an external site., MASM11

 

Entry requirements Science faculty:

For admission to the course knowledge equivalent to at least one of the courses
MASC03, Markov processes, 7.5 credits or MASC04, Stationary Stochastic processes,
7.5 credits are required together with English B.

Reading list

  • Goef H. Givens & Jennifer A. Hoeting: Computational Statistics, 2 ed. Wiley, 2013, ISBN: 978-0-470-53331-4.

Contact and other information

Director of studies: studierektor@matstat.lu.se
The number of participants is not limited.
The course overlaps following course/s:  FMS091 Links to an external site., MASM11

Official Course Description