Welcome to Randal Douc's wiki

A collaborative site on maths but not only!

User Tools

Site Tools

Trace: • homepage

Sidebar

Wiki

Wiki

Courses and public working groups

Courses and public working groups

Private Working Groups

Private Working Groups

Personal Notes

Personal Notes

Réponses

Réponses

Miscellanous

Miscellanous

world:homepage

Table of Contents

Welcome to Randal Douc's homepage

  • 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:
    1. randal.douc“arobase”telecom-sudparis.eu
    2. randal.douc“arobase”polytechnique.edu

Publications

I co-authored the following two books:

  1. Book: R. Douc, E. Moulines and D. Stoffer: Non Linear Time series: Theory, Methods and Applications with R Examples. Wiley Edition, 2014.
  2. 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:

Click here to see my published papers

Click here to see 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
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 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 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 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 C. Andral, R. Douc, C.P. Robert The Importance Markov chain Stochastic Processes and their applications 2024 Vol 171, May 2024.
45 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. Arxiv 2023
46 R. Douc, P. Jacob, A. Lee, D. Vats Solving the Poisson equation using coupled Markov chains Submitted. Arxiv 2023
47 R. Douc, S. Le Corff Asymptotic convergence of iterative optimization algorithms Submitted. Arxiv 2023

Teaching materials

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

Education

Extra (Theater, Movies)

world/homepage.txt · Last modified: 2024/10/07 18:12 by rdouc

Page Tools

Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki