MISTIS Research team
Modelling and Inference of Complex and Structured Stochastic Systems
- Leader : Florence Forbes
- Type : Project team
- Research center(s) : Grenoble
- Field : Applied Mathematics, Computation and Simulation
- Theme : Optimization, machine learning and statistical methods
- Partner(s) : Institut polytechnique de Grenoble,Université de Grenoble Alpes
- Collaborator(s) : Laboratoire Jean Kuntzmann (LJK) (UMR5224)
Team presentationThe project-team aims at developing statistical methods for dealing with complex systems, complex models and complex data. Our applications consist mainly of image processing and spatial data problems with some applications in biology and medicine. Our approach is based on the statement that complexity can be handled by working up from simple local assumptions in a coherent way, defining a structured model, and that is the key to modelling, computation, inference and interpretation. The methods we consider involve mixture models, Markovian models, and more generally hidden structure models on one hand, and semi and non-parametric methods on the other hand.
Research themesWe mainly focus on two directions of research:
- How to deal with complex phenomenons, complex models and complex data. We propose to use structured models and methods allowing easy interpretations. We propose to develop model selection and approximation techniques for complexe structure models and to study dimension reduction techniques based on non linear data analysis.
- The theoretical and practical behaviour of methods. We focus on approximations justifications, asymptotic behaviour and convergence analysis.
Research teams of the same theme :
- BONUS - Big Optimization aNd UncertaintieS
- GEOSTAT - Geometry and Statistics in acquisition data
- INOCS - Integrated Optimization with Complex Structure
- MODAL - MOdel for Data Analysis and Learning
- RANDOPT - Randomized Optimisation
- REALOPT - Reformulations based algorithms for Combinatorial Optimization
- SELECT - Model selection in statistical learning
- SEQUEL - Sequential Learning
- SIERRA - Statistical Machine Learning and Parsimony
- TAU - TAckling the Underspecified