Supervisions

Teaching

Lecture notes

PhD Supervisions 

• Adrien Clarenne (2016-2019). Asymptotiques dans des modèles de boules aléatoires poissoniennes et non-poissoniennes, Université de Rennes 1.
• Abdoulaye Soumana Hima (2013-2017). Stochastic differential equations under G-expectation and applications, Université de Rennes 1. PhD thesis coadvised by Ying Hu.
• Florian Bouguet (2013-2016). Étude quantitative de processus de Markov déterministes par morceaux issus de la modélisation, Université de Rennes 1.  PhD thesis coadvised by Florent Malrieu.
• Renan Gobard (2012-2015). Fluctuations dans des modèles de boules aléatoires, Université de Rennes 1.
• Benjamin Laquerrière (2007-2012) : Interpolation et comparaison de certains processus stochastiques. Université de La Rochelle. PhD thesis coadvised by Nicolas Privault.

Teaching for 2022-2023 (Moodle)

• Markov chains and Martingales (M1, S1)
• Stochastic processes (M2, S1)
• Preparation of "agrégation externe" (S1)

Lecture notes (in french)

Undergraduated level in Mathematics 

• Probabilités in L2 math
  Random variables, law, expectation, variance, standard probabilistic laws, independence, conditioning. (Without measure theory background.)
• Intégrale de Riemann in L2 math 
  
• Intégrale de Lebesgue in L3 math 
  sigma algebra, measures, Lebesgue's integration, convergence theorems, convolution, L^p spaces, Fourier transform. 
• Probabilités en L3 math : rich ; light   very short summary
  Random variables and vectors, law and distribution, moments of random variable, independence, probabilistic convergences, LLN, CLT, Gaussian vectors. (With measure theory background.)
  

Undergraduated level in other fields

• Statistiques in IUT Biotech 2nd Year  
  Standard probabilistic laws, statistic estimation, hypothesis tests.
  

Graduated level in Mathematics

• Processus discrets in M1 math 
  Conditioning, Martingales and Markov chains.
  
• Processus gaussiens in M2 math/info 
  Gaussian vectors, Gaussian processes, Brownian motion, Itô's integral, fractional Brownian motion, applications.
• Processus stochastiques in M2 math 
  Gaussian processes, Brownian motion, (sub/super, local) martingales, semimartingales, stochastic integration.
• Calcul stochastique in M2 math 
  Itô's formula, Girsanov's formula, SDE, Diffusions.
  

(These notes -in french- are complements of lectures ; any other use is left under the own responsability of the user. In particular, the author declines any responsability regarding the content.)