ECTS
4 credits
Training Structure
College of Sciences
Description
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. Topics covered may include the following:
- Analysis of biological sequences: Probabilistic models of biological sequence evolution, Phylogenetic inference, Hidden Markov models for pattern detection, Graph models and inference of gene regulatory networks
- population dynamics: birth-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, competitive risk models, U-statistic-based tests; fertility data analysis models; medical diagnosis and ROC curves as an application of a U-statistic; meta-analyses.
- Statistics of Extremes and Applications to the Environment: Univariate and multivariate theory of extreme values: the law of maxima and high-threshold exceedances for random variables and vectors, extreme dependencies, estimation of extreme quantiles, risk analysis. Applications to environmental data: rainfall, wave height, temperatures, etc.
- Spatial Statistics: Introduction to the fundamentals of spatial forecasting and its applications. To cover a broad range of spatial statistics, this course will be structured around 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. Application to various practical cases. Random effects in generalized linear models.
Objectives
An Introduction to More Specialized Areas of Statistics and Stochastic Modeling
Mandatory Prerequisites
M1 SSD or equivalent, first-semester courses of the M2 SSD program
Recommended prerequisites: M1 SSD or equivalent, first-semester courses of the M2 SSD program
Additional Information
Hours:
Lectures: 6:00 p.m.
Tutorials:
Lab:
Fieldwork: