### Wiki

### Wiki

### Courses and public working groups

### Courses and public working groups

### Private Working Groups

### Private Working Groups

- Theatre
- Admin
- Research
- Teaching

- Theatre
- Admin
- Research
- Teaching

world:homepage

**Current position**: Full professor at Telecom Sudparis (since 2008). Part time “Professeur chargé de cours”, Ecole Polytechnique (since 2017).**Area of Interest**: Computational Statistics, Applied Probability, Machine Learning.**Keywords**: Hidden Markov Models, latent variable models, sequential Monte Carlo methods, Markov chains Monte Carlo, variational inference, statistical inference.**Email:**- randal.douc“arobase”telecom-sudparis.eu
- randal.douc“arobase”polytechnique.edu

I co-authored the following two books:

**Book**: R. Douc, E. Moulines and D. Stoffer: Non Linear Time series:*Theory, Methods and Applications with R Examples.*Wiley Edition, 2014.**Book**: R. Douc, E. Moulines, P. Priouret and P. Soulier:*Markov chains.*Springer Edition, 2018.

If you click below, you can find a list of my published papers:

Filter:

Id | Authors | Title | Journal | Year | References | Links |
---|---|---|---|---|---|---|

1 | R. Douc, C. Matias | Propriétés asymptotiques de l'estimateur de maximum de vraisemblance pour des modèles de Markov cachés généraux | C.R Académie des Sciences | 2000 | Série I, p.135-138 | |

2 | R. Douc, C. Matias | Asymptotics of the Maximum Likelihood Estimator for general Hidden Markov Models | Bernoulli | 2001 | Volume 7, no. 3, 381–420 | |

3 | O.Cappé, R.Douc, E.Moulines, C.Robert. | On the Convergence of the Monte-Carlo Maximum Likelihood for Latent Variable Models | Scandinavian Journal of Statistics | 2002 | Volume 29 issue 4, p. 615-635 | |

4 | R.Douc, E. Moulines, J. Rosenthal. | Quantitative bounds for geometric convergence rates of Markov chains | Annals of Applied Probability | 2004 | Volume 14, no. 4, 1643-1665 | |

5 | R. Douc, G. Fort, E. Moulines, P. Soulier. | Practical drift conditions for subgeometric rates of convergence | Annals of Applied Probability | 2004 | Volume 14, no. 3, 1353–1377. 60J10 | |

6 | R. Douc, E. Moulines, T. Ryden | Asymptotic properties of the maximum likelihood estimator in autoregressive models with Marko | Annals of Statistics | 2004 | Volume 32 , no. 5, 2254-2304 | |

7 | R. Douc, A. Guillin and J. Najim | Moderate deviation in particle filtering | Annals of Applied Probability | 2005 | Volume 15 , no. 1B, 587-614 | |

8 | R. Douc, E. Moulines and P. Soulier | Computable bounds for subgeometric ergodic Markov chains | Bernoulli | 2007 | Volume 13, Number 3 , 831-848 | |

9 | R. Douc, A. Guillin, J.M. Marin, C.P. Robert | Minimum variance importance sampling via Population Monte Carlo | Esaim P&S. | 2007 | Volume 11, pp. 427-447 | |

10 | R. Douc, A. Guillin, J.M. Marin, C.P. Robert. | Convergence of adaptive mixtures of importance sampling schemes | Annals of Statistics | 2007 | Volume 35, Number 1. | |

11 | O. Cappé, R. Douc, A. Guillin, J. M. Marin and C. Robert | Adaptive importance sampling in general mixture classes | Statistics and Computing | 2008 | Volume 18, Number 4, 447-459 | |

12 | O. Cappé, R. Douc, E. Moulines and J. Olsson. | Sequential Monte Carlo smoothing with application to parameter estimation in non linear state space models | Bernoulli. | 2008 | Volume 14, Number 1 , 155-179. | |

13 | R. Douc, A. Guillin and E. Moulines. | Bounds on regeneration times and limit theorems for subgeometric Markov chains | Annales de l'I.H.P | 2008 | Volume 44, Number 2, 239-257. | |

14 | R.Douc and E. Moulines | Limit theorems for weighted samples with applications to Sequential Monte Carlo Methods | Annals of Statistics | 2008 | Volume 36, Number 5 , 2344-2376 | |

15 | R. Douc, F. Roueff and P. Soulier | On the existence of ARCH(infini) processes | Stochastic Processes and their Applications | 2008 | Volume 118, Issue 5, 755-761 | |

16 | R. Douc, G. Fort, E. Moulines and P. Priouret | Forgetting of the initial distribution for Hidden Markov Models | Stochastic Processes and their Applications | 2009 | Volume 119, Number 4, Pages 1235-1256 | |

17 | R.Douc, E. Moulines and J. Olsson | Optimality of the auxillary particle filter | Probability and Mathematical Statistics. | 2009 | Volume 29, issue 1, pages 1-28 | |

18 | R. Douc, G. Fort and A. Guillin | Subgeometric rates of convergence of f-ergodic strong Markov processes | Stochastic Processes and their Applications. | 2009 | Volume 119 Number 3, 897-923. | |

19 | R. Douc, E. Moulines and Y. Ritov. | Forgetting of the initial condition for the filter in general state-space hidden Markov chain: a coupling approach | Electronic Journal of Probability | 2009 | Volume 14, pp 27-49. | |

20 | R. Douc, E. Gassiat, B. Landelle, E. Moulines | Forgetting the initial distribution in non-ergodic Hidden Markov models | Annals of Applied Probability | 2010 | Volume 20, Number 5, 1638-1662. | |

21 | R. Douc, E. Moulines, J. Olsson and R. Van Handel | Consistency of the maximum likelihood estimator for general hidden Markov models | Annals of Statistics | 2011 | Volume 39, Number 1, 474-513. | |

22 | R. Douc, C.P. Robert | A vanilla Rao-Blackwellisation of Metropolis Hastings algorithms | Annals of Statistics | 2011 | Volume 39, Number 1, 261-277 | |

23 | R. Douc, A. Garivier, E. Moulines, J. Olsson. | Sequential Monte Carlo smoothing for general state space hidden Markov models | Annals of Applied Probability | 2011 | Volume 21, Number 6 (2011), 2109-2145 | |

24 | M. Bédard, R. Douc and E. Moulines | Scaling analysis of multiple-try MCMC methods | Stochastic Processes and their Applications | 2012 | Volume 122, Issue 3, Pages 758-786 | |

25 | R. Douc and E. Moulines | Asymptotic properties of the maximum likelihood estimation in misspecified Hidden Markov models | Annals of Statistics | 2012 | Oct. 2012, Volume 40, Number 5 (2012), 2697-2732. | |

26 | R. Douc, P. Doukhan and E. Moulines | Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator | Stochastic Processes and their Applications | 2013 | Volume 123, Issue 7, July 2013, Pages 2620-2647 | |

27 | M. Bédard, R. Douc and E. Moulines | Scaling analysis of Delayed Rejection MCMC methods | Methodology and Computing in Applied ProbabilitY | 2014 | Volume 16, Issue 4, P 811-838 | |

28 | C. Dubarry, R. Douc | Calibrating the exponential Ornstein-Uhlenbeck multiscale stochastic volatility model | Quantitative finance | 2014 | Volume 14, Issue 3, p 443-456 | |

29 | R. Douc, E. Moulines and J. Olsson | Long-term stability of sequential Monte Carlo methods under verifiable conditions | Annals of Applied Probability | 2014 | Volume 24, No. 5, p 1767–1802 | |

30 | F. Maire, R. Douc, J. Olsson | Comparison of Asymptotic Variances of Inhomogeneous Markov Chains with Applications to Markov Chain Monte Carlo Methods | Annals of Statistics | 2014 | Volume 42, No. 4, p 1483–1510 | |

31 | R. Douc, J. Olsson and F. Maire | On the use of Markov chain Monte Carlo methods for the sampling of mixture models | Statistics and Computing | 2015 | Volume 25, Issue 1, pp 95-110 | |

32 | R. Douc, F. Roueff and T. Sim | Handy sufficient conditions for the convergence of the maximum likelihood estimator in observation-driven models | Lituanian Mathematical Journal | 2015 | Volume 55, Issue 3, pp 367-392 | |

33 | R. Douc, F. Lindsten and E. Moulines | Uniform ergodicity of the Particle Gibbs sampler | Scandinavian Journal of Statistics | 2015 | Volume 42, Issue 3, pp 775-797 | |

34 | R. Douc, F. Roueff and T. Sim | The maximizing set of the asymptotic normalized log-likelihood for partially observed Markov chains | Annals of Applied probability | 2016 | Volume 26, Number 4, 2357-2383. | |

35 | R. Douc, K. Fokianos and E. Moulines | Asymptotic properties of Quasi-Maximum Likelihood Estimators in Observation-Driven Time Series models | Electronic Journal of Statistics | 2017 | Volume 11, Number 2 (2017), 2707-2740. | |

36 | R. Douc and J. Olsson | Numerically stable online estimation of variance in particle filters | Bernoulli | 2019 | Volume 25, Number 2, 1504-1535 | |

37 | R. Douc, J. Olsson and F. Roueff | Posterior consistency for partially observed Markov models | Stochastic Processes and their applications | 2020 | Volume 130, Issue 2, february 2020, Pages 733-759 | |

38 | R. Douc, F. Roueff and T. Sim | Necessary and sufficient conditions for the identifiability of observation-driven models | Journal of Time Series Analysis (JTSA) | 2021 (march) | Volume 42, Issue 2, p140-160 | |

39 | R. Douc, F. Roueff, T. Sim | General-order observation-driven models: Ergodicity and consistency of the maximum likelihood estimator | Electronic Journal of Statistics | 2021. June | Vol. 15, No. 1, 3349-3393 | |

40 | K. Daudel, R. Douc, F. Portier | Infinite-dimensional gradient-based descent for alpha-divergence minimisation | Annals of Statistics | 2021. Oct. | Vol. 49, No. 4, 2250–2270 | |

41 | K. Daudel, R. Douc | Mixture weights optimisation for Alpha-Divergence Variational Inference | Advances in Neural Information Processing Systems, (Neurips) | 2021. Nov. | 34 | |

42 | M. Gerber, R. Douc | A Global Stochastic Optimization Particle Filter Algorithm | Biometrika | 2022 | Volume 109, Issue 4, December 2022, Pages 937–955. | |

43 | K. Daudel, R. Douc, F. Roueff | Monotonic alpha-divergence minimisation | JMLR | 2023 | Accepted | |

44 | R. Douc, A. Durmus, A. Enfroy, J. Olsson | Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm | Submitted | 2023 | ||

45 | R. Douc, P. Jacob, A. Lee, D. Vats | Solving the Poisson equation using coupled Markov chains | Submitted. Arxiv | 2023 | ||

46 | C. Andral, R. Douc, C.P. Robert | The Importance Markov chain | Arxiv | 2023 |

Name of the Course | Description | Lecture Notes | Useful links and keywords | Miscellanous |
---|---|---|---|---|

Chaînes de Markov | Cours M2 dans le master MDA | A course on Markov Chains: Advanced topics | ||

Eléments d'analyse et théorie de l'intégration | Cours tronc commun TSP | Lecture Notes | Measure, Integration, Residues theorem, Hilbert space, Fourier transform | |

Monte Carlo and advanced sampling methods | Majeure MSA, TSP. ITC. | Lecture Notes | Rejection, Importance Sampling, Control Variates, Stratification, Variance reduction | |

Introduction to the Extreme Value Theory | Majeure MSA, TSP. | Lecture Notes | Order Statistics, Fisher-Tippett-Gnedenko theorem, domain of attraction | |

Stochastic calculus for finance | Majeure MSA, TSP | Lecture Notes | Ito Calculus, stochastic integrals, stochastic differential equations, brownian motions | |

Monte Carlo with applications to finance | Majeure MSA, TSP | Lecture Notes | Brownian bridges, monte carlo, variance reduction, Euler schemes | |

Markov Chain Monte Carlo: Theory and Practical applications | Master M2DS | Lecture Notes | Mcmc methods, Metropolis Hastings, Pseudo marginal algorithms, Hamiltonian Monte Carlo | |

Fundations of Machine Learning | 3rd year, Ecole Polytechnique | |||

Aléatoire | 1ère année, Ecole Polytechnique |

world/homepage.txt · Last modified: 2023/01/21 18:49 by rdouc