FMSN60 / MASM18, 7,5 credits, A (Second Cycle)

General Information

Elective for:BME4, C4, D5-bg, E4-bg, F4, F4-bg, Pi4-ssr, Pi4-biek, Pi4-bam, MMSR2, R4, Master Mathematical Statistics
Language of instruction: The course will be given in English

Contents

Bayesian methods for stochastic modelling, classification and reconstruction. Random fields, Gaussian random fields, Kriging, Markov fields, Gaussian Markov random fields, non-Gaussian observationer. Covariance functions, multivariate techniques. Simulation methods for stochastic inference (Gibbs sampling). Applications in climate, environmental statistics, remote sensing, and spatial statistics.

Examination details

LTH:

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written and oral project presentation. The final grade is determined by the result of the project parts.

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: 0115. Name: Project Part 1.
Credits: 2,5. Grading scale: UG. Assessment: Written project report

NF:

0702 Project part 1, 2,5 hp
Grading scale: Fail, Pass
0703 Project part 2, 5,0 hp
Grading scale: Fail, Pass

Admission

Admission requirements LTH:

FMSF10 Stationary Stochastic Processes or FMSF15 Markov Processes or FMSF20 Mathematical Statistics, Basic Course or FMSF25 Mathematical Statistics - Complementary Project or FMSF32 Mathematical Statistics or FMSF45 Mathematical Statistics, Basic Course or FMSF50 Mathematical Statistics, Basic Course or FMSF55 Mathematical Statistics, Basic Course or FMSF70 Mathematical Statistics or FMSF75 Mathematical Statistics, Basic Course or FMSF80 Mathematical Statistics, Basic Course

Assumed prior knowledge: At least one course in Markov processes or Stationary stochastic processes. Matlab proficiency.
The number of participants is limited to: No

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

A. Gelfand, P. Diggle, P. Guttorp, M. Fuentes (Eds.): Handbook of Spatial Statistics. CRC Press Inc, 2010, ISBN: 9781420072877. Only parts of the book are used, available as e-book.

Contact and other information

Director of studies: studierektor@matstat.lu.se
The number of participants is not limited.

The course overlaps following course/s: FMS150, MASM13, MASM25

Official Course Description