From b0313d9dba8cc1ff8c7b1c01e3409c99ff7b1d2f Mon Sep 17 00:00:00 2001 From: Julien Chiquet Date: Thu, 29 Feb 2024 11:28:50 +0100 Subject: [PATCH] correct arxiv id --- _bibliography/preprint.bib | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/_bibliography/preprint.bib b/_bibliography/preprint.bib index 52bb1ed..b21363d 100644 --- a/_bibliography/preprint.bib +++ b/_bibliography/preprint.bib @@ -2,8 +2,7 @@ @preprint{preprint_stoehr title = {Composite likelihood inference for the Poisson log-normal model}, author = {Stoehr, Julien and Robin, Stephane}, year = {2024}, - arxiv = {abs/2402.14390v1}, - url = {https://arxiv.org/abs/2402.14390v1}, + arxiv = {2402.14390v1}, journal = {arXiv}, abstract = {Inferring parameters of a latent variable model can be a daunting task when the conditional distribution of the latent variables given the observed ones is intractable. Variational approaches prove to be computationally efficient but, possibly, lack theoretical guarantees on the estimates, while sampling based solutions are quite the opposite. Starting from already available variational approximations, we define a first Monte Carlo EM algorithm to obtain maximum likelihood estimators, focusing on the Poisson log-normal model which provides a generic framework for the analysis of multivariate count data. We then extend this algorithm to the case of a composite likelihood in order to be able to handle higher dimensional count data.} }