Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid-19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been - or will be - communicated to the students by the teachers, as soon as possible.
3 credits
22.5 h + 15.0 h
Q2
Teacher(s)
Bogaert Patrick;
Language
French
Main themes
Notions of spatial/temporal dependency and its effect on statistical estimation. Quantification and modelling of dependencies through space and time. Random fields theory. Prediction and simulation of correlated data. Mapping and forecasting methods.
Aims
At the end of this learning unit, the student is able to : | |
| 1 |
A the end of this activity, the student is able to : · Name, describe and explain the theoretical concepts underlying the stochastic approach for the analysis and the modeling of spatial and temporal data in an environmental framework;
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
This course will complete the basic notions already presented during the courses LBIR 1212 - Probability and Statistics (I) and LBIR 1315 - Probability and Statistics (II). The student will be able to analyze data that are correlated through space and time, as frequently encountered in the agro-environmental context. The course will emphasize the link between the general theory and the practical specificities of environmental data. It should allow the student to model such kind of processes and to use them in a mapping or forecasting context.
Teaching methods
Regular course and supervised practical exercises. Practical exercises will take place in a computer room using the Matlab software. Students will work in groups and will process a specific spatial data set. This personal work will be part of a printed report that must be defended during the examination.
Evaluation methods
The examination takes place in two parts : (1) written examination (about an hour); (2) oral examination with a defense of the project completed by the students (abour half an hour)
Other information
This course follows the LBIR 1212 and LBIR 1315 courses.
This course can be taught in English.
This course can be taught in English.
Online resources
Moodle
Faculty or entity
AGRO
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Forests and Natural Areas Engineering
Master [120] in Civil Engineering
Master [120] in Statistic: Biostatistics
Master [120] in Biology of Organisms and Ecology
Certificat d'université : Statistique et sciences des données (15/30 crédits)
Master [120] in Environmental Bioengineering
Master [120] in Agriculture and Bio-industries