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

Fast factorisation of probabilistic potentials and its application to approximate inference in Bayesian networks

We present an efficient procedure for factorising probabilistic potentials represented as probability trees. This new procedure is able to detect some regularities that cannot be captured by existing methods. In cases where an exact decomposition is not achievable, we propose a heuristic way to carry out approximate factorisations guided by a parameter called factorisation degree, which is fast to compute. We show how this parameter can be used to control the tradeoff between complexity and accuracy in approximate inference algorithms for Bayesian networks

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

Learning recursive probability trees from probabilistic potentials

A recursive probability tree (RPT) is an incipient data structure for representing the distributions in a probabilistic graphical model. RPTs capture most of the types of independencies found in a probability distribution. The explicit representation of these features using RPTs simplifies computations during inference. This paper describes a learning algorithm that builds a RPT from a probability distribution. Experiments prove that this algorithm generates a good approximation of the original distribution, thus making available all the advantages provided by RPT

Health benefits of zumba: A systematic review

El objetivo de la presente revisión sistemática fue resumir y analizar los beneficios para la salud, tanto a nivel físico como psicológico, de una actividad colectiva tan popular y practicada a día de hoy, como es el Zumba. Para ello, se realizó una búsqueda en la base de datos Medline/Pubmed para encontrar todos los estudios publicados hasta el 15 de noviembre de 2016 bajo la palabra clave “Zumba”. Se encontraron 15 publicaciones que cubrían los criterios de inclusión. Según el estado actual de la literatura científica, practicar Zumba reporta beneficios significativos a nivel antropométrico (disminuye el índice de masa corporal), en la composición corporal (disminuyendo la grasa corporal), en la condición física (aumenta el consumo máximo de oxígeno), a nivel de calidad de vida (autopercepción física y bienestar psicológico). Por todo ello, se pude concluir que practicar Zumba repercute positivamente para la salud, y se recomienda que sea llevada a cabo por un monitor, ya que los beneficios son mayores que cuando se práctica siguiendo las directrices de un DVD.The purpose of this systematic review was to summarize and analyse the health benefits, both physical and psychological, a collective activity so popular and practiced today as is the Zumba. For this purpose, a search was conducted in Medline/Pubmed database to find all the studies published until 15 November 2016, under the key word "Zumba". 15 publications covering the inclusion criteria were found. As it is current state of the literature on this topic, the main benefits of this activity occur significantly to anthropometric level (decreases the body mass index), body composition (decreases the body fat), fitness (increase the maximum oxygen consumption), as well as also the parameters of quality of life (physical self-perception and psychological well-being). Therefore, the conclusion is that the Zumba practice has a positive impact on health, and it is recomended to do it with an instructor because there are more benefits than to practice in front of a DVD.peerReviewe

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

Comparing Binary and Standard Probability Trees in Credal Networks Inference

Abstract This paper proposes the use of Binary Probability Trees in the propagation of credal networks. Standard and binary probability trees are suitable data structures for representing potentials because they allow to control the accuracy of inference algorithms by means of a threshold parameter. The choice of this threshold is a trade-off between accuracy and computing time. Binary trees enable the representation of finer-grained independences than probability trees. This leads to more efficient algorithms for credal networks with variables with more than two states. The paper shows experiments comparing binary and standard probability trees in order to demonstrate their performance

SEOM clinical guidelines for the treatment of small-cell lung cancer (SCLC) (2019)

Small-cell lung cancer (SCLC) accounts for 15% of lung cancers. Only one-third of patients are diagnosed at limited stage. The median survival remains to be around 15-20 months without significative changes in the strategies of treatment for many years. In stage I and IIA, the standard treatment is the surgery followed by adjuvant therapy with platinum-etoposide. In stage IIB-IIIC, the recommended treatment is early concurrent chemotherapy with platinum-etoposide plus thoracic radiotherapy followed by prophylactic cranial irradiation in patients without progression. However, in the extensive stage, significant advances have been observed adding immunotherapy to platinum-etoposide chemotherapy to obtain a significant increase in overall survival, constituting the new recommended standard of care. In the second-line treatment, topotecan remains as the standard treatment. Reinduction with platinum-etoposide is the recommended regimen in patients with sensitive relapse (≥ 3 months) and new drugs such as lurbinectedin and immunotherapy are new treatment options. New biomarkers and new clinical trials designed according to the new classification of SCLC subtypes defined by distinct gene expression profiles are necessary

Catálogo de lencería e vestiario

Catálogo onde se relacionan todos os artigos de lencería e vestiario utilizados polo Servizo Galego de Saúde, no que se describe de forma individual as características básicas dos artigos (cor, composición, gramaxe,...) e a serigrafíaCatálogo donde se relacionan todos los artículos de lencería y vestuario utilizados por el Servizo Galego de Saúde, en el que se describe de forma individual las características básicas de los artículos (color, composición, gramaje,...) y la serigrafí