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| - **Book**: R. Douc, E. Moulines, P. Priouret and P. Soulier: //Markov chains.// Springer Edition, 2018. | - **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: | + | <color /yellow> If you click below, you can find a list of my published papers: </color> |
| - | <hidden ====Click here to see my published papers====> | + | <hidden ====Click here to see my published papers====> |
| <sortable r1> | <sortable r1> | ||
| <searchtable> | <searchtable> | ||
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| | 36 | R. Douc and J. Olsson | Numerically stable online estimation of variance in particle filters | Bernoulli | 2019 | Volume 25, Number 2, 1504-1535 | | | | 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 | | | | 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 | | | + | | 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. June | Vol. 15, No. 1, 3349-3393 | | | + | | 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. Oct. | Vol. 49, No. 4, 2250–2270 | | | + | | 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 | | | | 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. | | | | 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. | | | | 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 | | | | + | | 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, P. Jacob, A. Lee, D. Vats | Solving the Poisson equation using coupled Markov chains | Submitted. [[https://arxiv.org/abs/2206.05691|Arxiv]] | 2023 | | | | + | | 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. [[https://arxiv.org/abs/2201.05002|Arxiv]] | 2023 | | | |
| - | | 46 | C. Andral, R. Douc, C.P. Robert | The Importance Markov chain | Submitted. [[https://arxiv.org/abs/2207.08271|Arxiv]] | 2023 | | | | + | | 46 | 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 | | | |
| | 47 | R. Douc, S. Le Corff | Asymptotic convergence of iterative optimization algorithms | Submitted. [[https://arxiv.org/pdf/2302.12544.pdf|Arxiv]] | 2023 | | | | | 47 | R. Douc, S. Le Corff | Asymptotic convergence of iterative optimization algorithms | Submitted. [[https://arxiv.org/pdf/2302.12544.pdf|Arxiv]] | 2023 | | | | ||
| <pagemod result>| @@Id@@ | @@Authors@@ | @@Title@@ | @@Journal@@ | @@Year@@ | @@References@@ | @@Links@@ | | <pagemod result>| @@Id@@ | @@Authors@@ | @@Title@@ | @@Journal@@ | @@Year@@ | @@References@@ | @@Links@@ | | ||
world/homepage.1695833757.txt.gz · Last modified: 2023/09/27 18:55 by rdouc
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