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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. | | | ||
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