====== Welcome to Randal Douc's homepage ======
{{ :world:randaldoucjuillet2021.jpg?200|}}
* **Current position**: Full professor at Telecom Sudparis (2008-current).
* **Former position**:
* Full time position: "Professeur chargé de cours", Ecole Polytechnique, 2001-2007.
* Part-time position: “Professeur chargé de cours”, Ecole Polytechnique, 2017-2023.
* **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
===== Publications =====
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:
^ 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 | |
| 28 | 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 | |
| 27 | C. Dubarry, R. Douc | Calibrating the exponential Ornstein-Uhlenbeck multiscale stochastic volatility model | Quantitative finance | 2013 | 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 | Journal of Machine Learning Research (JMLR) | 2023 | (62):1−76, 2023. Vol 24. | |
| 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. [[https://arxiv.org/abs/2201.05002|Arxiv]] | 2023 | | |
| 45 | R. Douc, P. Jacob, A. Lee, D. Vats | Solving the Poisson equation using coupled Markov chains | Submitted. [[https://arxiv.org/abs/2206.05691|Arxiv]] | 2023 | | |
| 46 | C. Andral, R. Douc, C.P. Robert | The Importance Markov chain | Submitted. [[https://arxiv.org/abs/2207.08271|Arxiv]] | 2023 | | |
| 47 | R. Douc, S. Le Corff | Asymptotic convergence of iterative optimization algorithms | Submitted. [[https://arxiv.org/pdf/2302.12544.pdf|Arxiv]] | 2023 | | |
| @@Id@@ | @@Authors@@ | @@Title@@ | @@Journal@@ | @@Year@@ | @@References@@ | @@Links@@ |
|
===== Teaching materials =====
^ Name of the Course ^ Description ^ Lecture Notes ^ Useful links and keywords ^ Miscellanous ^
| Chaînes de Markov | Cours M2 dans le master MDA | | [[world:markovchains|A course on Markov Chains: Advanced topics]] | |
| Eléments d'analyse et théorie de l'intégration | Cours tronc commun TSP | {{ :world:polymaths.pdf | Lecture Notes}} | Measure, Integration, Residues theorem, Hilbert space, Fourier transform | |
| Monte Carlo and advanced sampling methods | Majeure MSA, TSP. ITC. | {{ :world:polymcenglish.pdf | Lecture Notes}} | Rejection, Importance Sampling, Control Variates, Stratification, Variance reduction | |
| Introduction to the Extreme Value Theory | Majeure MSA, TSP. | {{ :world:polyxtremes.pdf | Lecture Notes}} | Order Statistics, Fisher-Tippett-Gnedenko theorem, domain of attraction | |
| Stochastic calculus for finance | Majeure MSA, TSP | {{ :world:polycalculstofinance.pdf | Lecture Notes}} | Ito Calculus, stochastic integrals, stochastic differential equations, brownian motions | |
| Monte Carlo with applications to finance | Majeure MSA, TSP | {{ :world:polymcfinanceenglish.pdf | Lecture Notes}} | Brownian bridges, monte carlo, variance reduction, Euler schemes | |
| Markov Chain Monte Carlo: Theory and Practical applications | Master M2DS | {{ :world:polymcmc.pdf | 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 | | | |
===== Education =====
* [[world:education|Please follow the link]]
===== Extra (Theater, Movies) =====
* [[world:extra|Please follow the link]]