Search results for "Graphical model"
showing 10 items of 52 documents
Model selection for penalized Gaussian Graphical Models
2013
High-dimensional data refers to the case in which the number of parameters is of one or more order greater than the sample size. Penalized Gaussian graphical models can be used to estimate the conditional independence graph in high-dimensional setting. In this setting, the crucial issue is to select the tuning parameter which regulates the sparsity of the graph. In this paper, we focus on estimating the "best" tuning parameter. We propose to select this tuning parameter by minimizing an information criterion based on the generalized information criterion and to use a stability selection approach in order to obtain a more stable graph. The performance of our method is compared with the state…
Covariate adjusted censored gaussian lasso estimator
2021
The covariate adjusted glasso is one of the most used estimators for in- ferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data.
SPARSE INFERENCE IN COVARIATE ADJUSTED CENSORED GAUSSIAN GRAPHICAL MODELS
2021
The covariate adjusted glasso is one of the most used estimators for inferring genetic networks. Despite its diffusion, there are several fields in applied research where the limits of detection of modern measurement technologies make the use of this estimator theoretically unfounded, even when the assumption of a multivariate Gaussian distribution is satisfied. In this paper we propose an extension to censored data.
A computational method to estimate sparse multiple Gaussian graphical models
2012
In recent years several researchers have proposed the use of the Gaussian graphical model defined on a high dimensional setting to explore the dependence relationships between random variables. Standard methods, usually proposed in literature, are based on the use of a specific penalty function, such as the L1-penalty function. In this paper our aim is to estimate and compare two or more Gaussian graphical models defined in a high dimensional setting. In order to accomplish our aim, we propose a new computational method, based on glasso method, which lets us to extend the notion of p-value.
Genetic Network construction in CML gene expression profile data analysis
2009
Aim of this paper is to define a new statistical framework to identify central modules in Gaussian Graphical Models (GGMs) estimated by gene expression data measured on a sample of patients with negative molecular response to imatinib. A central module is defined as a module of a GGM which contains genes that are defined differentially expressed.
Graphical information models as interfaces for Web document repositories
2000
In interorganisational processes, documents are used to record information created during the processes. Legislative processes involving several legislative organisations, or manufacturing processes involving complicated networks of companies and officials are examples of such processes. In the contemporary computerised environments a great deal of the recorded information is scattered in different kinds of Web repositories with different kinds of interfaces. The repositories should serve as valuable knowledge assets but their use may be difficult and even the knowledge about the kinds of repositories available may be insufficient. The paper presents a method for improving information manag…
A New Environment for Distributed Multiple Vehicles Dynamics Control and Simulation
2007
Although there are many simulation softwares, the 3D multi-vehicle field needs further study. This paper describes a lightweight, full portable software environment for development, simulation and control of vehicles. This environment simulates ground wheeled vehicles and their environment. Multi vehicle study can be done adding the vehicle mathematical model and the 3D graphical model. The scenario is displayed as a 3D virtual reality environment in which all the objects are rendered using a combination of 3D primitive and/or pre-built 3D objects loaded from file. Since the software is made up of a set of multi-treading object-oriented Java classes, there are many advantages as portability…
Comparison of Different Hypotheses Regarding the Spread of Alzheimer’s Disease Using Markov Random Fields and Multimodal Imaging
2018
Alzheimer’s disease (AD) is characterized by a cascade of pathological processes that can be assessed in vivo using different neuroimaging methods. Recent research suggests a systematic sequence of pathogenic events on a global biomarker level, but little is known about the associations and dependencies of distinct lesion patterns on a regional level. Markov random fields are a probabilistic graphical modeling approach that represent the interaction between individual random variables by an undirected graph. We propose the novel application of this approach to study the interregional associations and dependencies between multimodal imaging markers of AD pathology and to compare different hy…
Probabilistic graphical model identifies clusters of EEG patterns in recordings from neonates
2018
Abstract Objectives In this paper we introduce a novel method for the evaluation of neonatal brain function via multivariate EEG (electroencephalography) signal processing and embedding into a probabilistic graph, the so called Chow-Liu tree. Methods Using 28 EEG recordings of preterm and term neonate infants the complex features of the EEG signals were constructed in the form of a Chow-Liu tree. The trees were embedded into a 3 dimensional Euclidean space. Clustering of specific EEG patterns was done by complete linkage algorithm. Results Our analytic tool was able to build clusters of patients with pathological EEG findings. In particular, we were able to make a visual proof on a 3d multi…
Graphical models for estimating dynamic networks
2012
Het bepalen van dynamische netwerken met behulp van data is een actief onderzoeksgebied, met name in de systeem biologie. Het schatten van de structuur van een netwerk heeft te maken met het bepalen van de aan of afwezigheid van een relatie tussen de hoekpunten in de graaf. Grafische modellen definiëren deze relaties via conditionele afhankelijkheid. In Gaussiaanse grafische modellen (GGM) wordt verondersteld dat de hoekpunten een normale verdeling volgen. Dit heeft grote voordelen vanwege de computationele handelbaarheid van GGM. Standaard GGM zijn echter niet bruikbaar om grote netwerken te bestuderen, i.e. als het aantal waarnemingen minder is dan het aantal hoekpunten van de graaf. Rece…