|Academic Profile |
| || |
Prof Nicolas Privault
Professor, School of Physical & Mathematical Sciences
Programme Director, MSc in Analytics Programme
|Prior to joining NTU, Nicolas Privault had been teaching at the universities of Evry, La Rochelle, and Poitiers in France.|
|Stochastic analysis, probability, mathematical finance|
- Computational Methods for Fredholm Determinants and Discrete Random Networks
- Construction of Derivation Operators for Spatial Poisson Processes and Applications to Portfolio Hedging and Control
- Construction of Two-Dimensional Stochastic Bridges and Applications in Image Analysis
- Counting and Weighing the Isomorphic Sub-Graphs of a Random Graph by Normal Approximation and Multiple Stochastic Integrals
- Determinantal point process for modelling RF energy harvesting networks
- Feynman-Kac Stochastic Particle Models and their Applications to Automotive Active Safety 360° Multisensor Fusion Networks
- Functional Inequalities for Spatial Point Processes and Applications
- Probabilistic Representations of Nonlinear PDEs Using Backward Stochastic Differential Equations - Application to Blow-up and Stability
- Robust Impulse Control with Ambiguity Aversion and Applications
- Stochastic Analysis for Point Processes and Applications to Wireless Networks
- I. Polak and N. Privault. (2019). Cournot games with limited demand: from multiple equilibria to stochastic equilibrium. Applied Mathematics and Optimization, .
- N. Privault. (2019). Third cumulant Stein approximation for Poisson stochastic integrals. Journal of Theoretical Probability, .
- B. Kızıldemir and N. Privault. (2018). Supermodular ordering of binomial, Poisson and Gaussian random vectors by tree-based correlations. Probability and Mathematical Statistics, 38, 385-405.
- N. Privault. (2018). Stein approximation for multidimensional Poisson random measures by third cumulant expansions. ALEA - Latin American Journal of Probability and Mathematical Statistics, 15, 1141-1161.
- I. Flint, N. Privault, and G.L. Torrisi. (2018). Bounds in total variation distance for discrete-time processes on the sequence space. Potential Analysis, .
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