Graphical Independence Networks with the gRain Package for R

In this paper we present the R package gRain for propagation in graphical independence networks (for which Bayesian networks is a special instance). The paper includes a description of the theory behind the computations. The main part of the paper is an illustration of how to use the package. The paper also illustrates how to turn a graphical model and data into an independence networ

Formal Cellular Machinery

International audienceVarious calculi have been proposed to model diff erent levels of abstraction of cell signaling and molecular interactions. In this paper we propose a framework inspired by some of these calculi that structures interactions and agents from the most basic elements of the cell (protein interaction sites) to higher order ones (compartments and molecular species)

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

The AMP Markov property is a recently proposed alternative Markov property for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced LWF Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.Comment: 15 pages, article will appear in Scandinavian Journal of Statistic

Binary Models for Marginal Independence

Log-linear models are a classical tool for the analysis of contingency tables. In particular, the subclass of graphical log-linear models provides a general framework for modelling conditional independences. However, with the exception of special structures, marginal independence hypotheses cannot be accommodated by these traditional models. Focusing on binary variables, we present a model class that provides a framework for modelling marginal independences in contingency tables. The approach taken is graphical and draws on analogies to multivariate Gaussian models for marginal independence. For the graphical model representation we use bi-directed graphs, which are in the tradition of path diagrams. We show how the models can be parameterized in a simple fashion, and how maximum likelihood estimation can be performed using a version of the Iterated Conditional Fitting algorithm. Finally we consider combining these models with symmetry restrictions

Prólogo de la 7ma edición

En esta edición, así como también podría decirse gratamente sobre las anteriores, se pueden observar rasgos que destacan este espacio de publicaciones. Por un lado, nos encontramos con diversas reflexiones sobre prácticas artísticas desde su metodología, desde el cómo se desarrollan las mismas junto con la valiosa reflexión teórica. Esto implica detenerse, acompañar esos procesos creativos desde la unidad teórico-práctica fortaleciendo ambos aspectos del campo artístico. De este modo, se van construyendo bases epistemológicas de análisis sin determinaciones dicotómicas y estructuradas sobre lo qué es o no es que empujarían a definiciones estancas; sino una apertura a la reflexión de cómo están siendo lo que ya son con su propio dinamismo. Otro de los rasgos que se destaca en estas publicaciones es el de la vinculación entre las prácticas artísticas y la tecnología; vinculación que tiene lugar desde un proceso de creación y experimentación, desde una perspectiva de dominio de la técnica al servicio del artista.Facultad de Arte

Editorial

Editorial El Peldaño Nª20  Año 202

Editorial

Editoria

Editorial

Editorial El Peldaño Nª20  Año 202