Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Massam, Hélène"'
Recent advances in biological research have seen the emergence of high-throughput technologies with numerous applications that allow the study of biological mechanisms at an unprecedented depth and scale. A large amount of genomic data is now distrib
Externí odkaz:
http://arxiv.org/abs/2005.04139
We consider multivariate centered Gaussian models for the random variable $Z=(Z_1,\ldots, Z_p)$, invariant under the action of a subgroup of the group of permutations on $\{1,\ldots, p\}$. Using the representation theory of the symmetric group on the
Externí odkaz:
http://arxiv.org/abs/2004.03503
We consider a class of colored graphical Gaussian models obtained by placing symmetry constraints on the precision matrix in a Bayesian framework. The prior distribution on the precision matrix is the colored $G$-Wishart prior which is the Diaconis-Y
Externí odkaz:
http://arxiv.org/abs/2004.00764
Autor:
Letac, Gérard, Massam, Hélène
For a given positive random variable $V>0$ and a given $Z\sim N(0,1)$ independent of $V$, we compute the scalar $t_0$ such that the distance between $Z\sqrt{V}$ and $Z\sqrt{t_0}$ in the $L^2(\R)$ sense, is minimal. We also consider the same problem i
Externí odkaz:
http://arxiv.org/abs/1810.02036
Gaussian graphical models are relevant tools to learn conditional independence structure between variables. In this class of models, Bayesian structure learning is often done by search algorithms over the graph space. The conjugate prior for the prec
Externí odkaz:
http://arxiv.org/abs/1706.04416
The R package (R Core Team (2016)) genMOSS is specifically designed for the Bayesian analysis of genome-wide association study data. The package implements the mode oriented stochastic search (MOSS) procedure as well as a simple moving window approac
Externí odkaz:
http://arxiv.org/abs/1611.07537
Distributed estimation methods have recently been used to compute the maximum likelihood estimate of the precision matrix for large graphical Gaussian models. Our aim, in this paper, is to give a Bayesian estimate of the precision matrix for large gr
Externí odkaz:
http://arxiv.org/abs/1605.08441
Publikováno v:
Ann. Statist. 47 (2019), no. 3, 1203--1233
The existence of the maximum likelihood estimate in hierarchical loglinear models is crucial to the reliability of inference for this model. Determining whether the estimate exists is equivalent to finding whether the sufficient statistics vector $t$
Externí odkaz:
http://arxiv.org/abs/1603.04843
Graphical Gaussian models with edge and vertex symmetries were introduced by \citet{HojLaur:2008} who also gave an algorithm to compute the maximum likelihood estimate of the precision matrix for such models. In this paper, we take a Bayesian approac
Externí odkaz:
http://arxiv.org/abs/1506.04347
Autor:
Massam, Helene, Wang, Nanwei
We consider two connected aspects of maximum likelihood estimation of the parameter for high-dimensional discrete graphical models: the existence of the maximum likelihood estimate (mle) and its computation. When the data is sparse, there are many ze
Externí odkaz:
http://arxiv.org/abs/1504.05434