Valid for: 2013/14
Decided by: Education Board B
Date of Decision: 2013-04-10
Elective for: C4, D5, D5-bg, E4, E4-mt, E4-bg, F4, F4-mt, F4-bg, Pi4, Pi4-mrk, Pi4-ssr
Language of instruction: The course will be given in English on demand
The aim of the course is to provide the student with tools for handling high-dimensional statistical problems. The course contains models, and methods with practical applications, mainly for spatial statistics and image analysis. Of special importance are the Bayesian aspects, since they form the foundation for many modern spatial statistical and image analysis methods. The course emphasises methods with appications in climate, environmental statistics, and remote sensing.
Knowledge and understanding
For a passing grade the student must
Competences and skills
For a passing grade the student must
Judgement and approach
For a passing grade the student must
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 (MCMC, etc.). Applications in climate, environmental statistics, remote sensing, and spatial statistics.
Grading scale: TH
Assessment: Written and oral project presentation.
Required prior knowledge: A course in Image analysis or at least one course in Markov processes or Stationary stochastic processes. Matlab proficiency.
The number of participants is limited to: No
The course overlaps following course/s: FMS150, MAS228, MASM13
Director of studies: Studierektor Anna Lindgren, studierektor@matstat.lu.se
Course homepage: http://www.maths.lth.se/matstat/kurser/fmsn20/
Further information: The course is also given at the faculty of science with the code MASM25.