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R and Big Data
Thursday 26 - Friday 27 September 2019
The main aims of this course are to introduce participants to Big Data and the similarities and differences between regular modeling approaches and big data modeling, to help them understand the possibilities and limitations of R in big data research, to introduce them to high performance computing and to reproducible research. This course is aimed at experienced R users and should not be seen as a course to learn R.
Monday 2 - Friday 6 December 2019
Geostatistics is concerned with the analysis and modelling of spatial variability. It also addresses how quantified spatial variability can be used in optimal spatial interpolation and spatial stochastic simulation. Fields of application include hydrology, soil science, ecology, geology, agriculture, and forestry.
Statistical Uncertainty Analysis of Dynamic Models
Monday 9 - Friday 13 December 2019
The purpose of this course is to make the participants familiar with general statistical concepts describing uncertainty, and methods to compute prediction uncertainty coming from uncertain parameter values. We introduce dynamic input-state-output systems and methods to write your model in this format.