Zobrazeno 1 - 10
of 253
pro vyhledávání: '"Lavielle Marc"'
Publikováno v:
In Computers in Biology and Medicine November 2024 182
The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because the whole data set is required at each iteration of the algorithm. To al
Externí odkaz:
http://arxiv.org/abs/1910.12521
The ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely used to perform such sampling, but this method is known to converge slow
Externí odkaz:
http://arxiv.org/abs/1910.12222
Autor:
Karimi, Belhal, Lavielle, Marc
The ability to generate samples of the random effects from their conditional distributions is fundamental for inference in mixed effects models. Random walk Metropolis is widely used to conduct such sampling, but such a method can converge slowly for
Externí odkaz:
http://arxiv.org/abs/1910.12090
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Logistic regression is a common classification method in supervised learning. Surprisingly, there are very few solutions for performing logistic regression with missing values in the covariates. We suggest a complete approach based on a stochastic ap
Externí odkaz:
http://arxiv.org/abs/1805.04602
Autor:
Dowek, Antoine, Lê, Laetitia Minh Mai, Rohmer, Tom, Legrand, François-Xavier, Remita, Hynd, Lampre, Isabelle, Tfayli, Ali, Lavielle, Marc, Caudron, Eric
Publikováno v:
In Talanta 1 September 2020 217
Publikováno v:
In Computational Statistics and Data Analysis May 2020 145
Publikováno v:
In Computational Statistics and Data Analysis January 2020 141:123-138