Bibliography

Major publications by the team in recent years

• 1L. Beznea, M. Deaconu, O. Lupascu.
  
  Branching processes for the fragmentation equation, in: Stochastic Processes and their Applications, 2015, vol. 125, pp. 1861-1885. [ DOI : 10.1016/j.spa.2014.11.016 ]
  
  https://hal.inria.fr/hal-00948876
• 2M. Bossy, J.-F. Jabir.
  
  Lagrangian stochastic models with specular boundary condition, in: Journal of Functional Analysis, March 2015, vol. 268, n^o 6, pp. 1309–1381.
  
  https://hal.inria.fr/hal-00875040
• 3M. Bossy, N. Maïzi, O. Pourtallier.
  
  Game theory analysis for carbon auction market through electricity market coupling, in: Commodities, Energy and Environmental Finance, M. Ludkovski, R. Sircar, R. Aid (editors), Fields Institute Communications, Springer, 2015, vol. 74, pp. 335-370. [ DOI : 10.1007/978-1-4939-2733-3_13 ]
  
  https://hal-mines-paristech.archives-ouvertes.fr/hal-01162832
• 4N. Champagnat, S. Méléard.
  
  Polymorphic evolution sequence and evolutionary branching, in: Probab. Theory Related Fields, 2011, vol. 151, n^o 1-2, pp. 45–94.
  
  http://dx.doi.org/10.1007/s00440-010-0292-9
• 5N. Champagnat, D. Villemonais.
  
  Exponential convergence to quasi-stationary distribution and Q-process, in: Probability Theory and Related Fields, 2016, vol. 164, n^o 1, pp. 243-283, 46 pages. [ DOI : 10.1007/s00440-014-0611-7 ]
  
  https://hal.archives-ouvertes.fr/hal-00973509
• 6B. Cloez, C. Fritsch.
  
  Gaussian approximations for chemostat models in finite and infinite dimensions, in: Journal of Mathematical Biology, October 2017, vol. 75, n^o 4, pp. pp. 805-843.
  
  https://hal.archives-ouvertes.fr/hal-01371591
• 7L. Coutin, A. Lejay.
  
  Perturbed linear rough differential equations, in: Annales mathématiques Blaise Pascal, April 2014, vol. 21, n^o 1, pp. 103-150.
  
  https://hal.inria.fr/hal-00722900
• 8M. Deaconu, S. Herrmann.
  
  Hitting time for Bessel processes—walk on moving spheres algorithm (WoMS), in: Ann. Appl. Probab., 2013, vol. 23, n^o 6, pp. 2259–2289.
  
  http://dx.doi.org/10.1214/12-AAP900
• 9F. Delarue, J. Inglis, S. Rubenthaler, E. Tanré.
  
  Global solvability of a networked integrate-and-fire model of McKean-Vlasov type, in: Annals of Applied Probability, January 2015, vol. 25, n^o 4, pp. 2096–2133, Version 4: shortened version.
  
  https://hal.inria.fr/hal-00747565
• 10J. Inglis, D. Talay.
  
  Mean-field limit of a stochastic particle system smoothly interacting through threshold hitting-times and applications to neural networks with dendritic component, in: SIAM Journal on Mathematical Analysis, 2015, vol. 47, n^o 15, pp. 3884–3916. [ DOI : 10.1137/140989042 ]
  
  https://hal.inria.fr/hal-01069398
• 11A. Lejay.
  
  The snapping out Brownian motion, in: Annals of Applied Probability, September 2015.
  
  https://hal.inria.fr/hal-00781447

Publications of the year

Doctoral Dissertations and Habilitation Theses

• 12R. Maftei.
  
  Stochastic Analysis for Lagrangian Particles Simulation, Université Côte D'Azur ; Ecole Doctorale n°364 « Sciences Fondamentales et Appliquées », January 2017.
  
  https://hal.archives-ouvertes.fr/tel-01681293
• 13V. Reutenauer.
  
  Stochastic algorithms for risk management and indexing of database media, Université Côte d'Azur, March 2017.
  
  https://tel.archives-ouvertes.fr/tel-01544854

Articles in International Peer-Reviewed Journals

• 14M. Baar, A. Bovier, N. Champagnat.
  
  From stochastic, individual-based models to the canonical equation of adaptive dynamics - In one step, in: The Annals of Applied Probability : an official journal of the institute of mathematical statistics, 2017, vol. 27, n^o 2, pp. 1093-1170, https://arxiv.org/abs/1505.02421v2. [ DOI : 10.1214/16-AAP1227 ]
  
  https://hal.inria.fr/hal-01188189
• 15M. Bossy, J.-F. Jabir.
  
  Particle approximation for Lagrangian Stochastic Models with specular boundary condition, in: Electronic Communications in Probability, 2018, https://arxiv.org/abs/1504.07296, forthcoming.
  
  https://hal.inria.fr/hal-01147441
• 16P. C. Bressloff, O. C. Faugeras.
  
  On the Hamiltonian structure of large deviations in stochastic hybrid systems, in: Journal of Statistical Mechanics: Theory and Experiment, 2017, vol. 2017, 33206 p. [ DOI : 10.1088/1742-5468/aa64f3 ]
  
  https://hal.inria.fr/hal-01072077
• 17F. Campillo, N. Champagnat, C. Fritsch.
  
  On the variations of the principal eigenvalue with respect to a parameter in growth-fragmentation models, in: Communications in Mathematical Sciences, 2017, vol. 15, n^o 7, pp. 1801-1819, https://arxiv.org/abs/1601.02516.
  
  https://hal.inria.fr/hal-01254053
• 18N. Champagnat, P.-E. Jabin.
  
  Strong solutions to stochastic differential equations with rough coefficients, in: The Annals of Probability, 2017, forthcoming.
  
  https://hal.inria.fr/hal-00799242
• 19N. Champagnat, D. Villemonais.
  
  Exponential convergence to quasi-stationary distribution for absorbed one-dimensional diffusions with killing, in: ALEA : Latin American Journal of Probability and Mathematical Statistics, 2017, vol. 14, pp. 177-199.
  
  https://hal.inria.fr/hal-01217843
• 20N. Champagnat, D. Villemonais.
  
  Uniform convergence of conditional distributions for absorbed one-dimensional diffusions, in: Journal of Applied Probability, 2017, https://arxiv.org/abs/1506.02385, forthcoming.
  
  https://hal.inria.fr/hal-01166960
• 21N. Champagnat, D. Villemonais.
  
  Uniform convergence of penalized time-inhomogeneous Markov processes, in: ESAIM: Probability and Statistics, 2017, https://arxiv.org/abs/1603.07477, forthcoming.
  
  https://hal.inria.fr/hal-01290222
• 22N. Champagnat, D. Villemonais.
  
  Uniform convergence to the Q-process, in: Electronic Communications in Probability, 2017, vol. 22, n^o 33, pp. 1-7, https://arxiv.org/abs/1611.02473. [ DOI : 10.1214/17-ECP63 ]
  
  https://hal.inria.fr/hal-01395727
• 23B. Cloez, C. Fritsch.
  
  Gaussian approximations for chemostat models in finite and infinite dimensions, in: Journal of Mathematical Biology, October 2017, vol. 75, n^o 4, pp. 805-843, https://arxiv.org/abs/1609.07951.
  
  https://hal.archives-ouvertes.fr/hal-01371591
• 24L. Coutin, A. Lejay.
  
  Sensitivity of rough differential equations: an approach through the Omega lemma, in: Journal of Differential Equations, 2017, pp. 1-22, https://arxiv.org/abs/1712.04705, forthcoming. [ DOI : 10.1016/j.jde.2017.11.031 ]
  
  https://hal.inria.fr/hal-00875670
• 25M. Deaconu, S. Herrmann.
  
  Initial-boundary value problem for the heat equation - A stochastic algorithm, in: Annals of Applied Probability, 2017, https://arxiv.org/abs/1610.03963, forthcoming.
  
  https://hal.archives-ouvertes.fr/hal-01380365
• 26M. Deaconu, S. Herrmann.
  
  Simulation of hitting times for Bessel processes with non-integer dimension, in: Bernoulli, November 2017, vol. 23, n^o 4B, pp. 3744 - 3771, https://arxiv.org/abs/1401.4843. [ DOI : 10.3150/16-BEJ866 ]
  
  https://hal.archives-ouvertes.fr/hal-00933198
• 27M. Deaconu, S. Herrmann, S. Maire.
  
  The walk on moving spheres: a new tool for simulating Brownian motion's exit time from a domain, in: Mathematics and Computers in Simulation, May 2017, vol. 135, pp. 28–38, https://arxiv.org/abs/1401.3695.
  
  https://hal.archives-ouvertes.fr/hal-00931816
• 28M. Deaconu, A. Lejay, K. Salhi.
  
  Approximation of CVaR minimization for hedging under exponential-Lévy models, in: Journal of Computational and Applied Mathematics, May 2017, vol. 326, pp. 171-182.
  
  https://hal.inria.fr/hal-01461215
• 29P. Del Moral, D. Villemonais.
  
  Exponential mixing properties for time inhomogeneous diffusion processes with killing, in: Bernoulli, January 2018, vol. 24, n^o 2, pp. 1010-1032, https://arxiv.org/abs/1412.2627.
  
  https://hal.archives-ouvertes.fr/hal-01083297
• 30P. Del Moral, D. Villemonais.
  
  Exponential mixing properties for time inhomogeneous diffusion processes with killing, in: Bernoulli, May 2018, vol. 24, n^o 2, pp. 1010 - 1032, https://arxiv.org/abs/1412.2627. [ DOI : 10.3150/16-BEJ845 ]
  
  https://hal.inria.fr/hal-01593882
• 31C. Fritsch, F. Campillo, O. Ovaskainen.
  
  A numerical approach to determine mutant invasion fitness and evolutionary singular strategies, in: Theoretical Population Biology, 2017, vol. 115, pp. 89-99, https://arxiv.org/abs/1612.04049. [ DOI : 10.1016/j.tpb.2017.05.001 ]
  
  https://hal.archives-ouvertes.fr/hal-01413638
• 32A. Kohatsu-Higa, A. Lejay, K. Yasuda.
  
  Weak rate of convergence of the Euler-Maruyama scheme for stochastic differential equations with non-regular drift, in: Journal of Computational and Applied Mathematics, 2017, vol. 326C, pp. 138-158. [ DOI : 10.1016/j.cam.2017.05.015 ]
  
  https://hal.inria.fr/hal-00840211
• 33A. Lejay.
  
  Estimation of the bias parameter of the skew random walk and application to the skew Brownian motion, in: Statistical Inference for Stochastic Processes, 2017. [ DOI : 10.1007/s11203-017-9161-9 ]
  
  https://hal.inria.fr/hal-01319319
• 34A. Lejay.
  
  A Monte Carlo estimation of the mean residence time in cells surrounded by thin layers, in: Mathematics and Computers in Simulation, 2018, vol. 143C, pp. 65-77. [ DOI : 10.1016/j.matcom.2017.05.008 ]
  
  https://hal.inria.fr/hal-01216471
• 35A. Lejay, P. Pigato.
  
  Statistical estimation of the Oscillating Brownian Motion, in: Bernoulli, September 2017, https://arxiv.org/abs/1701.02129.
  
  https://hal.archives-ouvertes.fr/hal-01430794
• 36W. Oçafrain, D. Villemonais.
  
  Convergence of a non-failable mean-field particle system, in: Stochastic Analysis and Applications, March 2017, vol. 35, n^o 4, pp. 587-603, https://arxiv.org/abs/1606.08978. [ DOI : 10.1080/07362994.2017.1288136 ]
  
  https://hal.archives-ouvertes.fr/hal-01338421
• 37K. Salhi.
  
  Pricing European options and risk measurement under Exponential Lévy models - a practical guide, in: International Journal of Financial Engineering, 2017, vol. 2, n^o 2 & 3, 1750016 (36 pages) p.
  
  https://hal.inria.fr/hal-01322698

International Conferences with Proceedings

• 38B. Dumortier, E. Vincent, M. Deaconu.
  
  Recursive Bayesian estimation of the acoustic noise emitted by wind farms, in: 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New Orleans, United States, March 2017.
  
  https://hal.inria.fr/hal-01428962

Scientific Books (or Scientific Book chapters)

• 39A. Lejay.
  
  The Girsanov theorem without (so much) stochastic analysis, in: Séminaire de Probabilités XLIX, 2017, forthcoming.
  
  https://hal.inria.fr/hal-01498129
• 40A. Richard, D. Talay.
  
  Noise sensitivity of functionals of fractional Brownian motion driven stochastic differential equations: Results and perspectives, in: Modern Problems of Stochastic Analysis and Statistics, V. Panov (editor), Springer, November 2017, pp. 219-236, forthcoming.
  
  https://hal.inria.fr/hal-01620377

Internal Reports

• 41A. Lejay, P. Pigato.
  
  Data and methods for A threshold model for local volatility: evidence of leverage and mean reversion effects on historical data, Inria Nancy - Grand Est ; Weierstrass Institute, December 2017, n^o RT-0494.
  
  https://hal.inria.fr/hal-01668975

Other Publications

• 42R. Azaïs, A. Genadot, B. Henry.
  
  Inference for conditioned Galton-Watson trees from their Harris path, June 2017, https://arxiv.org/abs/1609.01057 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01360650
• 43L. Beznea, M. Deaconu, O. Lupascu.
  
  Numerical approach for stochastic differential equations of fragmentation; application to avalanches, December 2017, working paper or preprint.
  
  https://hal.inria.fr/hal-01667319
• 44J. Bion-Nadal, D. Talay.
  
  On a Wasserstein-type distance between solutions to stochastic differential equations, November 2017, working paper or preprint.
  
  https://hal.inria.fr/hal-01636082
• 45M. Bossy, J. Fontbona, H. Olivero.
  
  Synchronization of stochastic mean field networks of Hodgkin-Huxley neurons with noisy channels, January 2018, working paper or preprint.
  
  https://hal.inria.fr/hal-01678710
• 46M. Bossy, J.-F. Jabir, R. Maftei.
  
  Convergence rate analysis of time discretization scheme for confined Lagrangian processes, December 2017, working paper or preprint.
  
  https://hal.inria.fr/hal-01656716
• 47N. Champagnat, K. A. Coulibaly-Pasquier, D. Villemonais.
  
  Criteria for exponential convergence to quasi-stationary distributions and applications to multi-dimensional diffusions, January 2018, https://arxiv.org/abs/1603.07909 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01293622
• 48N. Champagnat, B. Henry.
  
  A probabilistic approach to Dirac concentration in nonlocal models of adaptation with several resources, November 2017, https://arxiv.org/abs/1711.10732 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01651468
• 49N. Champagnat, D. Villemonais.
  
  General criteria for the study of quasi-stationarity, 2017, https://arxiv.org/abs/1712.08092 - working paper or preprint.
  
  https://hal.inria.fr/hal-01672979
• 50N. Champagnat, D. Villemonais.
  
  Lyapunov criteria for uniform convergence of conditional distributions of absorbed Markov processes, April 2017, https://arxiv.org/abs/1704.01928 - working paper or preprint.
  
  https://hal.inria.fr/hal-01503697
• 51Q. Cormier.
  
  Study of the limit equation associated to a model of interacting neurons, Inria - Sophia Antipolis, July 2017.
  
  https://hal.inria.fr/hal-01635840
• 52C. Coron, S. Méléard, D. Villemonais.
  
  Perpetual integrals convergence and extinctions in population dynamics, April 2017, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01514977
• 53P. Helson.
  
  A new stochastic STDP Rule in a neural Network Model, November 2017, https://arxiv.org/abs/1706.00364 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01651745
• 55J.-F. Jabir, D. Talay, M. Tomasevic.
  
  Mean–field limit of a particle approximation of the one-dimensional parabolic–parabolic Keller-Segel model without smoothing, January 2018, https://arxiv.org/abs/1712.07490 - working paper or preprint.
  
  https://hal.inria.fr/hal-01668926
• 56A. Lejay, L. Lenôtre, G. Pichot.
  
  Analytic expressions of the solutions of advection-diffusion problems in 1D with discontinuous coefficients, November 2017, working paper or preprint.
  
  https://hal.inria.fr/hal-01644270
• 57A. Lejay, E. Mordecki, S. Torres.
  
  Two consistent estimators for the Skew Brownian motion, May 2017, working paper or preprint.
  
  https://hal.inria.fr/hal-01492853
• 58A. Lejay, P. Pigato.
  
  A threshold model for local volatility: evidence of leverage and mean reversion effects on historical data, December 2017, https://arxiv.org/abs/1712.08329 - working paper or preprint.
  
  https://hal.inria.fr/hal-01669082
• 59V. Reutenauer, E. Tanré.
  
  An unbiased Monte Carlo estimator for derivatives. Application to CIR, February 2017, https://arxiv.org/abs/1609.07431 - working paper or preprint.
  
  https://hal.inria.fr/hal-01371448
• 60A. Richard, P. Orio, E. Tanré.
  
  An integrate-and-fire model to generate spike trains with long memory, May 2017, https://arxiv.org/abs/1702.03762 - working paper or preprint.
  
  https://hal.inria.fr/hal-01521891
• 61D. Talay, M. Tomasevic.
  
  A new McKean-Vlasov stochastic interpretation of the parabolic-parabolic Keller-Segel model: The one-dimensional case, January 2018, https://arxiv.org/abs/1712.10254 - working paper or preprint.
  
  https://hal.inria.fr/hal-01673332
• 62D. Villemonais.
  
  Lower bound for the coarse Ricci curvature of continuous-time pure jump processes, November 2017, https://arxiv.org/abs/1705.06642 - working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-01652879

References in notes

• 63A. Beskos, O. Papaspiliopoulos, G. O. Roberts.
  
  Retrospective exact simulation of diffusion sample paths with applications, in: Bernoulli, 2006, vol. 12, n^o 6, pp. 1077–1098.
• 64P. Cattiaux, S. Méléard.
  
  Competitive or weak cooperative stochastic Lotka-Volterra systems conditioned on non-extinction, in: J. Math. Biol., 2010, vol. 60, n^o 6, pp. 797–829.
  
  https://doi.org/10.1007/s00285-009-0285-4
• 65N. Fournier, E. Löcherbach.
  
  On a toy model of interacting neurons, in: Ann. Inst. Henri Poincaré Probab. Stat., 2016, vol. 52, n^o 4, pp. 1844–1876.
  
  https://doi.org/10.1214/15-AIHP701