Search results for "Graphical model"

cglasso: An R Package for Conditional Graphical Lasso Inference with Censored and Missing Values

2023

Sparse graphical models have revolutionized multivariate inference. With the advent of high-dimensional multivariate data in many applied fields, these methods are able to detect a much lower-dimensional structure, often represented via a sparse conditional independence graph. There have been numerous extensions of such methods in the past decade. Many practical applications have additional covariates or suffer from missing or censored data. Despite the development of these extensions of sparse inference methods for graphical models, there have been so far no implementations for, e.g., conditional graphical models. Here we present the general-purpose package cglasso for estimating sparse co…

Graphical Models for Dependencies and Associations

1992

The role of graphical representations is described in distinguishing various special forms of independency structure that can arise with multivariate data, especially in observational studies in the social sciences. Conventions for constructing the graphs and strategies for analysing three sets of data are summarized. Finally some directions for desirable future work are outlined.

Statistical Dependence and Independence

2005

Statistical dependence is a type of relation between different characteristics measured on the same units. At one extreme is deterministic dependence; at the other is statistical independence, where the distribution of one variable is the same for all levels of the other. With more than two variables, an important distinction is between marginal and conditional dependence. In many contexts, the degree of dependence may be summarized by a suitable measure of association, perhaps as part of a general model. Reference is made to graphical models. Keywords: association; correlation; marginal; conditional; exponential family; graphical Markov models

Model transformation language MOLA

2005

The paper describes a new graphical model transformation language MOLA. The basic idea of MOLA is to merge traditional structured programming as a control structure with pattern-based transformation rules. The key language element is a graphical loop concept. The main goal of MOLA is to describe model transformations in a natural and easy readable way.

An adaptive probabilistic approach to goal-level imitation learning

2010

Imitation learning has been recognized as a promising technique to teach robots advanced skills. It is based on the idea that robots could learn new behaviors by observing and imitating the behaviors of other skilled actors. We propose an adaptive probabilistic graphical model which copes with three core issues of any imitative behavior: observation, representation and reproduction of skills. Our model, Growing Hierarchical Dynamic Bayesian Network (GHDBN), is hierarchical (i.e. able to characterize structured behaviors at different levels of abstraction), and growing (i.e. skills are learned or updated incrementally - and at each level of abstraction - every time a new observation sequence…

An adaptive probabilistic graphical model for representing skills in PbD settings

2010

Learning Bayesian Metanetworks from Data with Multilevel Uncertainty

2006

Managing knowledge by maintaining it according to dynamic context is among the basic abilities of a knowledge-based system. The two main challenges in managing context in Bayesian networks are the introduction of contextual (in)dependence and Bayesian multinets. We are presenting one possible implementation of a context sensitive Bayesian multinet-the Bayesian Metanetwork, which implies that interoperability between component Bayesian networks (valid in different contexts) can be also modelled by another Bayesian network. The general concepts and two kinds of such Metanetwork models are considered. The main focus of this paper is learning procedure for Bayesian Metanetworks.

GHOST: GRADIENT HISTOGRAM OF SPECTRAL TEXTURE

2021

International audience; A gradient-based texture feature for hyperspectral image is formulated with straightforward application to grayscale and color images. Processed in full band, GHOST is expressed as a four-dimensional probability density distribution encompassing joint metrological assessment of spectral and spatial properties. Its performance is close to Opponent Band Local Binary Pattern (OBLBP) in HyTexiLa texture classification (91 %-99 % accuracy) with feature size 0.2 % of OBLBP's.

Determinants of individual tourist expenditure as a network: Empirical findings from Uruguay

2014

Abstract This paper introduces the use of graphical models for assessing the determinants of individual tourist spending. These models have the advantage of synthesizing and visualizing the relationships occurring within large sets of random variables, through an easy to interpret output. To this end, individual data from a large official survey of international tourists in Uruguay are used. Symmetric conditional independence structures are first investigated. Then subgraphs of each expenditure item's neighbourhood are extracted in order to assess the impact of main effects and interactions through proportional ordinal logistic regression. Results highlight the marginal role of socio-demogr…

Machine learning for mortality analysis in patients with COVID-19

2020

This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching…