Reasoning with imprecise probabilities

This special issue of the International Journal of Approximate Reasoning (IJAR) grew out of the 4th International Symposium on Imprecise Probabilities and Their Applications (ISIPTA’05), held in Pittsburgh, USA, in July 2005 (http://www.sipta.org/isipta05). The symposium was organized by Teddy Seidenfeld, Robert Nau, and Fabio G. Cozman, and brought together researchers from various branches interested in imprecision in probabilities. Research in artificial intelligence, economics, engineering, psychology, philosophy, statistics, and other fields was presented at the meeting, in a lively atmosphere that fostered communication and debate. Invited talks by Isaac Levi and Arthur Dempster enlightened the attendants, while tutorials by Gert de Cooman, Paolo Vicig, and Kurt Weichselberger introduced basic (and advanced) concepts; finally, the symposium ended with a workshop on financial risk assessment, organized by Teddy Seidenfeld

Computation of Kullback–Leibler Divergence in Bayesian Networks

Kullback–Leibler divergence KL(p, q) is the standard measure of error when we have a true probability distribution p which is approximate with probability distribution q. Its efficient computation is essential in many tasks, as in approximate computation or as a measure of error when learning a probability. In high dimensional probabilities, as the ones associated with Bayesian networks, a direct computation can be unfeasible. This paper considers the case of efficiently computing the Kullback–Leibler divergence of two probability distributions, each one of them coming from a different Bayesian network, which might have different structures. The paper is based on an auxiliary deletion algorithm to compute the necessary marginal distributions, but using a cache of operations with potentials in order to reuse past computations whenever they are necessary. The algorithms are tested with Bayesian networks from the bnlearn repository. Computer code in Python is provided taking as basis pgmpy, a library for working with probabilistic graphical models.Spanish Ministry of Education and Science under project PID2019-106758GB-C31European Regional Development Fund (FEDER

Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks

This paper proposes two new algorithms for inference in credal networks. These algorithms enable probability intervals to be obtained for the states of a given query variable. The first algorithm is approximate and uses the hill-climbing technique in the Shenoy–Shafer architecture to propagate in join trees; the second is exact and is a modification of Rocha and Cozman’s branch-and-bound algorithm, but applied to general directed acyclic graphs.TIN2004-06204-C03-0

Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions

Ministerio de Economía y Competitividad y Fondo Europeo de Desarrollo Regional (FEDER), proyectos TEC2015-69496-R y TIN2016-77902-C3-2-

Value‐based potentials: Exploiting quantitative information regularity patterns in probabilistic graphical models

This study was jointly supported by the Spanish Ministry of Education and Science under projects PID2019-106758GB-C31 and TIN2016-77902-C3-2-P, and the European Regional Development Fund (FEDER). Funding for open access charge from Universidad de Granada/CBUA.When dealing with complex models (i.e., models with many variables, a high degree of dependency between variables, or many states per variable), the efficient representation of quantitative information in probabilistic graphical models (PGMs) is a challenging task. To address this problem, this study introduces several new structures, aptly named value‐based potentials (VBPs), which are based exclusively on the values. VBPs leverage repeated values to reduce memory requirements. In the present paper, they are compared with some common structures, like standard tables or unidimensional arrays, and probability trees (PT). Like VBPs, PTs are designed to reduce the memory space, but this is achieved only if value repetitions correspond to context‐specific independence patterns (i.e., repeated values are related to consecutive indices or configurations). VBPs are devised to overcome this limitation. The goal of this study is to analyze the properties of VBPs. We provide a theoretical analysis of VBPs and use them to encode the quantitative information of a set of well‐known Bayesian networks, measuring the access time to their content and the computational time required to perform some inference tasks.Spanish Government PID2019-106758GB-C31 TIN2016-77902-C3-2-PEuropean Commissio

Using Value-Based Potentials for Making Approximate Inference on Probabilistic Graphical Models

The computerization of many everyday tasks generates vast amounts of data, and this has lead to the development of machine-learning methods which are capable of extracting useful information from the data so that the data can be used in future decision-making processes. For a long time now, a number of fields, such as medicine (and all healthcare-related areas) and education, have been particularly interested in obtaining relevant information from this stored data. This interest has resulted in the need to deal with increasingly complex problems which involve many different variables with a high degree of interdependency. This produces models (and in our case probabilistic graphical models) that are difficult to handle and that require very efficient techniques to store and use the information that quantifies the relationships between the problem variables. It has therefore been necessary to develop efficient structures, such as probability trees or value-based potentials, to represent the information. Even so, there are problems that must be treated using approximation since this is the only way that results can be obtained, despite the corresponding loss of information. The aim of this article is to show how the approximation can be performed with value-based potentials. Our experimental work is based on checking the behavior of this approximation technique on several Bayesian networks related to medical problems, and our experiments show that in some cases there are notable savings in memory space with limited information loss.Spanish Government PID2019-106758GB-C31European CommissionUniversidad de Granada/CBU

Máster en Ciencia de Datos e Ingeniería de Computadores: una apuesta por la formación especializada en el sector de las TIC

En el curso 2014-2015 se ha impartido por vez primera el “Máster en Ciencia de Datos e Ingeniería de Computadores” en la Universidad de Granada. Este máster surge de la unión de los anteriores “Máster en Soft Computing y Sistemas Inteligentes” y “Máster en Ingeniería de Computadores y Redes”, renovando completamente sus contenidos y ofreciendo una oportunidad de asociación entre varios grupos de investigación con un enorme potencial en el ámbito de las Tecnologías de la Información y de las Comunicaciones (TIC). Este máster proporciona al estudiante la oportunidad de formarse profesionalmente en campos con una gran demanda de personal cualificado y de enorme interés social, tales como el análisis de grandes cantidades de datos (big data) o el desarrollo de sistemas de propósito específico y plataformas de computación de altas prestaciones.The Master ’s Degree in Data Science and Computer Engineering has been taught at the University of Granada for the first time during academic year 2014-2015. This master comes from the combination of former masters in Soft Computing and Computer Engineering, but it completely renovates its contents and it provid es an opportunity for partnership and collaboration between several research groups with a great potential in the field of Information Technology and Communications (ICT). The proposed Master ’s Degree trains students as researchers in areas with a high demand for qualified staff and huge social interest, such as the analysis of large amounts of data (big data) or the development of embedded systems and platforms for high-performance computing.Universidad de Granada: Departamento de Arquitectura y Tecnología de Computadores; Vicerrectorado para la Garantía de la Calidad

Post-Franco Theatre

In the multiple realms and layers that comprise the contemporary Spanish theatrical landscape, “crisis” would seem to be the word that most often lingers in the air, as though it were a common mantra, ready to roll off the tongue of so many theatre professionals with such enormous ease, and even enthusiasm, that one is prompted to wonder whether it might indeed be a miracle that the contemporary technological revolution – coupled with perpetual quandaries concerning public and private funding for the arts – had not by now brought an end to the evolution of the oldest of live arts, or, at the very least, an end to drama as we know it

Propagación aproximada de intervalos de probabilidad en grafos de dependencias

Las redes bayesianas han sido usadas muy frecuentemente para la construcción de sistemas expertos bayesianos. Estos sistemas expertos trabajan con valores de probabilidad precisos. Para un experto resulta muy difícil el dar una gran cantidad de probabilidades precisas a la hora de construir el sistema experto. Debido a ello en esta tesis se propone el uso de intervalos de probabilidad para representar la incertidumbre. Existen algoritmos exactos de propagación de intervalos de probabilidad sobre redes que transforman los intervalos en conjuntos convexos de probabilidad para poder obtener resultados finales correctos. Estos algoritmos son bastante complejos, y en la práctica sólo son capaces de resolver problemas muy simples. Por tanto, en esta tesis se han construído algoritmos aproximados de propagación en grafos de dependencias, en los que las distribuciones vienen dadas por intervalos de probabilidad. Los algoritmos construídos han utilizado técnicas de optimización combinatoria tales como el enfriamiento simulado y los algoritmos genéticos. También hemos utilizado los árboles de probabilidad para representar y operar con los distintos potenciales haciendo la propagación aún más eficiente y permitiendo adaptarnos a la capacidad de memoria de nuestro ordenador. Los árboles de probabilidad han permitido adaptarnos a la capacidad de memoria de nuestro ordenador a la hora de realizar los cálculos.Tesis Univ. Granada. Departamento de Ciencias de la Computación e Inteligencia ArtificialTrabajo financiado en parte por la Comunidad Económica Europea, proyecto Esprit III Drums II Bra 6156, y el proyecto "Entorno para el desarrollo de modelos gráficos probabilísticos CICYT (TIC97-1135-C04-01)

Using probability trees to compute marginals with imprecise probabilities

This paper presents an approximate algorithm to obtain a posteriori intervals of probability, when available information is also given with intervals. The algorithm uses probability trees as a means of representing and computing with the convex sets of probabilities associated to the intervals