Supplement 1

The supplementary courses provide an introduction to more specialized areas of statistics and stochastic modeling. Their content is subject to change from year to year. The topics covered may include the following:
- Analysis of biological sequences: Probabilistic models of biological sequence evolution, Phylogeny inference, Hidden Markov models for pattern detection, Graphical models and inference of gene regulation networks
     - Population dynamics: birth and death processes (definitions, properties, asymptotic behavior, parameter estimation, simulation), deterministic, stochastic, or hybrid approximations
- Biomedical statistics: Introduction to clinical research data, regulatory and methodological aspects, likelihood function and applications to biomedical data, review of survival data, competing risks models, U-statistic-based testing, fertility data analysis models, medical diagnosis and ROC curves as an application of a U-statistic, meta-analyses.
     - Extreme statistics and applications to the environment: Theory of univariate and multivariate extreme values: law of maxima and high threshold exceedances for random variables and vectors, extremal dependencies, estimation of extreme quantiles, risk analysis. Applications for environmental data: rainfall, wave height, temperatures, etc.
    - Spatial statistics: Introduction to the fundamentals of spatial prediction and applications. In order to cover a wide range of spatial statistics, this course will be divided into two parts: point processes and geostatistics.
- Mixed linear models: Extension of linear models to mixed linear models. Estimation of fixed effect parameters such as variance within these models. Implementation in various practical cases. Random effects in generalized linear models.

Opening up to more specialized areas of statistics and stochastic modeling

 M1 SSD or equivalent, first semester courses of M2 SSD



Recommended prerequisites: M1 SSD or equivalent, first semester courses of M2 SSD

Hours per week:
CM: 18 hours
TD:
TP:
Fieldwork: