PE&RC Postgraduate courses

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Introduction to R for Statistical Analysis
Monday 23 & Tuesday 24 October 2017
The aim of this course is to provide an introduction to R, a language and environment for statistical computing and graphics. Focus of the course will be on getting familiar with the R environment, to use R for manipulation and exploration of data, and to perform simple statistical analyses. Hands-on exercises will form a large part of the workshop.
Bayesian Statistics
Wednesday 25 & Thursday 26 October 2017
Nowadays, with the advance of computing and Markov Chain Monte Carlo (MCMC) algorithms, Bayesian statistics is becoming a powerful alternative for traditional Frequentistic statistics. Participants will be surprised how easy they can tackle problems that are quite complicated to handle with traditional Frequentistic statistics.
Basic Statistics
11, 12, 13, 18, 19 December 2017
This is a refresher course. The level is that of a second course in Statistics. We will refresh basic knowledge of Probability, Statistical Inference (Estimation and Testing), t-tests, simple cases of Regression and ANOVA, Experimental Design, Nonparametric Tests, and Chi-square Tests. Some time is reserved to discuss statistical problems of the participants.
Statistical Uncertainty Analysis of Dynamic Models
Monday 11 - Friday 15 December 2017
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.
Stable isotope applications in microbiology and environmental studies
Monday 11 - Thursday 14 December 2017
Stable isotope concepts currently are being used for the investigation of microbial processes in natural and contaminated environments, for instance to provide insight in microbial networks and interactions. This four day course intends to give an overview of current stable isotope applications in environmental sciences and microbioogy.
Design of Experiments
Wednesday 20 - Friday 22 December 2017
The design and analysis of experiments, using plants, animals, or humans, are an important part of the scientific process. Proper design of an experiment, apart from its proper analysis and interpretation, is important to convince a researcher that your results are valid and that your conclusions are meaningful.
Structural Equation Modelling
Monday 22 - Friday 26 January 2018
While much of statistics focusses on associations between variables and making predictions, the aim of structural equation modelling is to establish causal relationships between variables. The focus will be on classical structural equation models with a small number of (latent) variables, but we will also give an introduction to recent developments on methodology for high-dimensional data.
Introduction to Zero Inflated Models with R | Frequentist and Bayesian approaches
Monday 29 January - Friday 2 February 2018
During the course several case studies are presented, in which the statistical theory for zero inflated models is integrated with applied analyses in a clear and understandable manner. Zero inflated models consist of two integrated GLMs and therefore we will start with a revision of GLM. Zero inflated GLMMs for nested data (repeated measurements, short time series, clustered data, etc.) are discussed in the second part of the course. We will focus on zero inflated count data, and zero inflated continuous data.
Geostatistics
Monday 5 - Friday 9 February 2018
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.
Survival Analysis
Thursday 22 & Friday 23 February 2018
In this short course, the concept of survival analysis will be introduced and it will be shown how to apply the methods to biological data. Main topics are how to handle censored data, estimation of Kaplan-Meier survivor curves, the Log-Rank test, and Cox' regression models for estimating and testing effects of covariates.