Bayesian deconvolution of Bernoulli-Gaussian processes. - Marc Lavielle (Signal Process., 1993)

Detection of changes in the spectrum of a multidimensional process. - Marc Lavielle (IEEE Trans. Signal Process., 1993)

On two-dimensional spectral realization. - Fabrice Gamboa; Marc Lavielle (IEEE Trans. Inf. Theory, 1994)

A stochastic algorithm for parametric and non-parametric estimation in the case of incomplete data. - Marc Lavielle (Signal Process., 1995)

A simulated annealing version of the EM algorithm for non-Gaussian deconvolution. - Marc Lavielle; Eric Moulines (Stat. Comput., 1997)

Optimal segmentation of random processes. - Marc Lavielle (IEEE Trans. Signal Process., 1998)

Simulation-based methods for blind maximum-likelihood filter identification. - Olivier Cappé; Arnaud Doucet; Marc Lavielle; Eric Moulines (Signal Process., 1999)

Gibbs sampling for parameters estimation and change-points detection in inverse problems. Application to electromagnetic imaging. - Marc Lavielle; D. Marquez (Signal Process., 1999)

An application of MCMC methods for the multiple change-points problem. - Marc Lavielle; E. Lebarbier (Signal Process., 2001)

Bayesian off-line detection of multiple change-points corrupted by multiplicative noise: application to SAR image edge detection. - Jean-Yves Tourneret; Michel Doisy; Marc Lavielle (Signal Process., 2003)

Selection of Temporal Models for Event-Related fMRI. - Sophie Donnet; Marc Lavielle; Philippe Ciuciu; Jean-Baptiste Poline (ISBI, 2004)

Blind source separation of noisy mixtures using a semi-parametric approach with application to heavy-tailed signals. - Mohamed Sahmoudi; Karim Abed-Meraim; Marc Lavielle; Estelle Kuhn; Philippe Ciblat (EUSIPCO, 2005)

A statistical approach for array CGH data analysis. - Franck Picard; Stéphane Robin; Marc Lavielle; Christian Vaisse; Jean-Jacques Daudin (BMC Bioinform., 2005)

Maximum likelihood estimation in nonlinear mixed effects models. - Estelle Kuhn; Marc Lavielle (Comput. Stat. Data Anal., 2005)

Using penalized contrasts for the change-point problem. - Marc Lavielle (Signal Process., 2005)

Semiparametric estimation of the frequency of unknown periodic functions and its application to laser vibrometry signals. - Marc Lavielle; Céline Lévy-Leduc (IEEE Trans. Signal Process., 2005)

Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model. - Adeline Samson; Marc Lavielle; France Mentré (Comput. Stat. Data Anal., 2006)

Are fMRI event-related response constant in time? A model selection answer. - Sophie Donnet; Marc Lavielle; Jean-Baptiste Poline (NeuroImage, 2006)

A parameter expansion version of the SAEM algorithm. - Marc Lavielle; Cristian Meza (Stat. Comput., 2007)

Anatomically Informed Bayesian Model Selection for fMRI Group Data Analysis. - Merlin Keller; Marc Lavielle; Matthieu Perrot; Alexis Roche (MICCAI (1), 2009)

Maximum likelihood estimation in discrete mixed hidden Markov models using the SAEM algorithm. - Maud Delattre; Marc Lavielle (Comput. Stat. Data Anal., 2012)

An improved SAEM algorithm for maximum likelihood estimation in mixtures of non linear mixed effects models. - Marc Lavielle; Cyprien Mbogning (Stat. Comput., 2014)

On the Global Convergence of (Fast) Incremental Expectation Maximization Methods. - Belhal Karimi; Hoi-To Wai; Eric Moulines; Marc Lavielle (NeurIPS, 2019)

Inheritance and variability of kinetic gene expression parameters in microbial cells: modeling and inference from lineage tree data. - Aline Marguet; Marc Lavielle; Eugenio Cinquemani (Bioinform., 2019)

Logistic regression with missing covariates - Parameter estimation, model selection and prediction within a joint-modeling framework. - Wei Jiang 027; Julie Josse; Marc Lavielle; TraumaBase Group (Comput. Stat. Data Anal., 2020)

f-SAEM: A fast stochastic approximation of the EM algorithm for nonlinear mixed effects models. - Belhal Karimi; Marc Lavielle; Eric Moulines (Comput. Stat. Data Anal., 2020)

Using hospital data for monitoring the dynamics of COVID-19 in France. - Marc Lavielle (J. Data Sci. Stat. Vis., 2022)

A joint analysis proposal of nonlinear longitudinal and time-to-event right-, interval-censored data for modeling pregnancy miscarriage. - Rolando De la Cruz; Marc Lavielle; Cristian Meza; Vicente Núñez-Antón (Comput. Biol. Medicine, 2024)