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While hidden class models of various types arise in many statistical applications, it is often difficult to establish the identifiability of their parameters. Focusing on models in which there is some structure of independence of some of the observed variables conditioned on hidden ones, we demonstrate a general approach for establishing identifiability… (More)

Our concern is selecting the concentrationmatrix’s nonzero coefficients for a sparse Gaussian graphical model in a high-dimensional setting. This corresponds to estimating the graph of conditional dependencies between the variables.We describe a novel framework taking into account a latent structure on the concentration matrix. This latent structure is used… (More)

This paper deals with parameter estimation in pair hidden Markov models (pairHMMs). We first provide a rigorous formalism for these models and discuss possible definitions of likelihoods. The model being biologically motivated, some restrictions with respect to the full parameter space naturally occur. Existence of two different Information divergence rates… (More)

- Julien Chiquet, Alexander Smith, Gilles Grasseau, Catherine Matias, Christophe Ambroise
- Bioinformatics
- 2009

SUMMARY
The R package SIMoNe (Statistical Inference for MOdular NEtworks) enables inference of gene-regulatory networks based on partial correlation coefficients from microarray experiments. Modelling gene expression data with a Gaussian graphical model (hereafter GGM), the algorithm estimates non-zero entries of the concentration matrix, in a sparse and… (More)

- C. Matias, S. Schbath, E. Birmelé
- 2006

• Network motifs are at the core of modern studies on biological networks, trying to encompass global features such as small-world or scale-free properties. Detection of significant motifs may be based on two different approaches: either a comparison with randomized networks (requiring the simulation of a large number of networks), or the comparison with… (More)

Abstract: We consider a semiparametric convolution model. We observe random variables having a distribution given by the convolution of some unknown density f and some partially known noise density g. In this work, g is assumed exponentially smooth with stable law having unknown selfsimilarity index s. In order to ensure identifiability of the model, we… (More)

While latent class models of various types arise in many statistical applications, it is often difficult to establish their identifiability. Focusing on models in which there is some structure of independence of some of the observed variables conditioned on hidden ones, we demonstrate a general approach for establishing identifiability, utilizing algebraic… (More)

This paper deals with order identification for Markov chains with Markov regime (MCMR) in the context of finite alphabets. We define the joint order of a MCMR process in terms of the number k of states of the hidden Markov chain and the memory m of the conditional Markov chain. We study the properties of penalized maximum likelihood estimators for the… (More)

We present a selective review on probabilistic modeling of heterogeneity in random graphs. We focus on latent space models and more particularly on stochastic block models and their extensions that have undergone major developments in the last five years. Résumé. Nous présentons une revue non exhaustive de la modélisation probabiliste de l’hétérogénéité des… (More)