28 citations · 63 across the 7 of their papers we have counts for
9 papers
On the application of Gaussian graphical models to paired data problems
Saverio Ranciati, Alberto Roverato
Gaussian graphical models are nowadays commonly applied to the comparison of groups sharing the same variables, by jointy learning their independence structures. We consider the ca…
Exploration of the search space of Gaussian graphical models for paired data
Alberto Roverato, Dung Ngoc Nguyen
We consider the problem of learning a Gaussian graphical model in the case where the observations come from two dependent groups sharing the same variables. We focus on a family of…
Fused graphical lasso for brain networks with symmetries
Saverio Ranciati, Alberto Roverato, Alessandra Luati
Neuroimaging is the growing area of neuroscience devoted to produce data with the goal of capturing processes and dynamics of the human brain. We consider the problem of inferring…
Path Weights in Concentration Graphs
Alberto Roverato, Robert Castelo
A graphical model provides a compact and efficient representation of the association structure of a multivariate distribution by means of a graph. Relevant features of the distribu…
The networked partial correlation and its application to the analysis of genetic interactions
Alberto Roverato, Robert Castelo
Genetic interactions confer robustness on cells in response to genetic perturbations. This often occurs through molecular buffering mechanisms that can be predicted using, among ot…
Log-mean linear regression models for binary responses with an application to multimorbidity
Monia Lupparelli, Alberto Roverato
In regression models for categorical data a linear model is typically related to the response variables via a transformation of probabilities called the link function. We introduce…
Mapping eQTL networks with mixed graphical Markov models
Inma Tur, Alberto Roverato, Robert Castelo
Expression quantitative trait loci (eQTL) mapping constitutes a challenging problem due to, among other reasons, the high-dimensional multivariate nature of gene-expression traits.…
Dichotomization invariant log-mean linear parameterization for discrete graphical models of marginal independence
Alberto Roverato
We extend the log-mean linear parameterization introduced by Roverato et al. (2013) for binary data to discrete variables with arbitrary number of levels, and show that also in thi…
Log-mean linear models for binary data
Alberto Roverato, Monia Lupparelli, Luca La Rocca
This paper introduces a novel class of models for binary data, which we call log-mean linear models. The characterizing feature of these models is that they are specified by linear…