Sequential Bayesian Inference Methods for Complex Dynamical Systems
Many problems in different scientific domains can be described through statistical models that relate the sequential observed data to a hidden process through some unobserved parameters. In the Bayesian framework, the probabilistic estimation of the unknowns is represented by the posterior distribution of these parameters. However, in most of the realistic models, the posterior is intractable and must be approximated. Importance Sampling (IS)-based algorithms are Monte Carlo methods that have shown a satisfactory performance in many problems of Bayesian inference.
In this thesis, we will study IS-based methods for probabilistic inference in complex non-linear high-dimensional systems. More specifically, we will propose novel adaptation schemes in order to overcome current limitations of more traditional IS-based techniques in such a challenging context. Many applications can be benefited from the development of these methodologies, including inference in gene regulatory networks, indoor localization problems, spatial-temporal field reconstruction, among many others.
*WHERE AND WHEN*
Two fully funded PhD position are available in Lille from September/October 2018 at IMT Lille Douai and laboratory CRIStAL. Earlier start date can be also considered. Lille is a vibrant, young and dynamic city. Lille lies in the heart of the triangle that links three of Europe’s main metropoles: London (80 min), Paris (60 min), and Brussels (35 min).
The students will be supervised by:
– Víctor Elvira: http://pagesperso.telecom-lille.fr/elvira/
– François Septier: http://pagesperso.telecom-lille.fr/septier/
We are looking for two motivated and talented PhD students with:
– background in signal processing, statistics or applied mathematics
– strong mathematical skills
– experience in programming, preferably in Matlab and/or Python.
* CONTACT *
If you have any question and/or want to apply, please contact:
– Víctor Elvira: email@example.com
– François Septier: firstname.lastname@example.org