Search results for "Graphical model"
showing 10 items of 52 documents
Sparse model-based network inference using Gaussian graphical models
2010
We consider the problem of estimating a sparse dynamic Gaussian graphical model with L1 penalized maximum likelihood of structured precision matrix. The structure can consist of specific time dynamics, known presence or absence of links in the graphical model or equality constraints on the parameters. The model is defined on the basis of partial correlations, which results in a specific class precision matrices. A priori L1 penalized maximum likelihood estimation in this class is extremely difficult, because of the above mentioned constraints, the computational complexity of the L1 constraint on the side of the usual positive-definite constraint. The implementation is non-trivial, but we sh…
Inferring slowly-changing dynamic gene-regulatory networks
2015
Dynamic gene-regulatory networks are complex since the interaction patterns between their components mean that it is impossible to study parts of the network in separation. This holistic character of gene-regulatory networks poses a real challenge to any type of modelling. Graphical models are a class of models that connect the network with a conditional independence relationships between random variables. By interpreting these random variables as gene activities and the conditional independence relationships as functional non-relatedness, graphical models have been used to describe gene-regulatory networks. Whereas the literature has been focused on static networks, most time-course experi…
Socio-economic inequality, interregional mobility and mortality among cancer patients: A mediation analysis approach
2022
This paper investigates the effect of socio-economic status on interregional mobility and mortality among cancer patients. The cohort under analysis comprises patients residing in Sicily (Italy), who were diagnosed with lung and colon cancer between 2010 and 2011. The data was collated from the hospital discharge records of the Sicilian Region and the Regional register of the causes of death, by considering all those patients for whom information relating to socio-economic status was available. First, graphical models were applied to highlight the multivariate structure of association among socio-economic status, interregional mobility and 3-year mortality. Secondly, mediation analysis quan…
Graphical models for estimating network determinants of multi-destination trips in Sicily
2017
Abstract This paper proposes a two-step approach for analysing the main determinants of multi-destination trip behaviour. It is based on a combination of graphical models and of a multinomial logistic regression model; the aim is to analyse direct and indirect effects of a wide set of tourist- and trip-related characteristics on multi-destination trip behaviour. Empirical data have been derived from a frontier survey of approximately 4000 incoming tourists in Sicily (Italy) at the end of their trip. Results suggest that multi-destination trips depend directly on the length of stay, the number of previous visits and motivation for the trip, and only indirectly on the interview month, travel …
Simplifying Probabilistic Expressions in Causal Inference
2018
Obtaining a non-parametric expression for an interventional distribution is one of the most fundamental tasks in causal inference. Such an expression can be obtained for an identifiable causal effect by an algorithm or by manual application of do-calculus. Often we are left with a complicated expression which can lead to biased or inefficient estimates when missing data or measurement errors are involved. We present an automatic simplification algorithm that seeks to eliminate symbolically unnecessary variables from these expressions by taking advantage of the structure of the underlying graphical model. Our method is applicable to all causal effect formulas and is readily available in the …
Grapham: Graphical models with adaptive random walk Metropolis algorithms
2008
Recently developed adaptive Markov chain Monte Carlo (MCMC) methods have been applied successfully to many problems in Bayesian statistics. Grapham is a new open source implementation covering several such methods, with emphasis on graphical models for directed acyclic graphs. The implemented algorithms include the seminal Adaptive Metropolis algorithm adjusting the proposal covariance according to the history of the chain and a Metropolis algorithm adjusting the proposal scale based on the observed acceptance probability. Different variants of the algorithms allow one, for example, to use these two algorithms together, employ delayed rejection and adjust several parameters of the algorithm…
Study design in causal models
2012
The causal assumptions, the study design and the data are the elements required for scientific inference in empirical research. The research is adequately communicated only if all of these elements and their relations are described precisely. Causal models with design describe the study design and the missing data mechanism together with the causal structure and allow the direct application of causal calculus in the estimation of the causal effects. The flow of the study is visualized by ordering the nodes of the causal diagram in two dimensions by their causal order and the time of the observation. Conclusions whether a causal or observational relationship can be estimated from the collect…
Cyclic coordinate for penalized Gaussian graphical models with symmetry restriction
2014
In this paper we propose two efficient cyclic coordinate algorithms to estimate structured concentration matrix in penalized Gaussian graphical models. Symmetry restrictions on the concentration matrix are particularly useful to reduce the number of parameters to be estimated and to create specific structured graphs. The penalized Gaussian graphical models are suitable for high-dimensional data.
Factorial graphical models for dynamic networks
2015
AbstractDynamic network models describe many important scientific processes, from cell biology and epidemiology to sociology and finance. Estimating dynamic networks from noisy time series data is a difficult task since the number of components involved in the system is very large. As a result, the number of parameters to be estimated is typically larger than the number of observations. However, a characteristic of many real life networks is that they are sparse. For example, the molecular structure of genes make interactions with other components a highly-structured and, therefore, a sparse process. Until now, the literature has focused on static networks, which lack specific temporal inte…
Metamodeling editor as a front end tool for a CASE shell
1992
Customizable Computer Aided Software Engineering (CASE) tools, often called CASE shells, are penetrating in the market. CASE shells provide a flexible environment to support a variety of information systems development methods. CASE shells are often cumbersome to use and in practice few people can model and implement methods in them. To overcome these problems we have developed a graphical metamodeling environment called MetaEdit and a method modeling interface to the CASE shell RAMATIC. Using this interface the methodology engineer can develop graphical models in RAMATIC's model definition language and then easily generate the resource files that control the operations of RAMATIC. MetaEdit…