Blankets Joint Posterior score for learning Markov network structures

• Autores: Schluter, Federico Enrique Adolfo; Strappa Figueroa, Yanela Daiana; Milone, Diego Humberto; Bromberg, Facundo
• Año de publicación: 2018
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: BLANKETS POSTERIOR; IRREGULAR STRUCTURES; MARKOV NETWORK; SCORING FUNCTION; STRUCTURE LEARNING
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

Markov networks are extensively used to model complex sequential, spatial, and relational interactions in a wide range of fields. By learning the Markov network independence structure of a domain, more accurate joint probability distributions can be obtained for inference tasks or, more directly, for interpreting the most significant relations among the variables. Recently, several researchers have investigated techniques for automatically learning the structure from data by obtaining the probabilistic maximum-a-posteriori structure given the available data. However, all the approximations proposed decompose the posterior of the whole structure into local sub-problems, by assuming that the posteriors of the Markov blankets of all the variables are mutually independent. In this work, we propose a scoring function for relaxing such assumption. The Blankets Joint Posterior score computes the joint posterior of structures as a joint distribution of the collection of its Markov blankets. Essentially, the whole posterior is obtained by computing the posterior of the blanket of each variable as a conditional distribution that takes into account information from other blankets in the network. We show in our experimental results that the proposed approximation can improve the sample complexity of state-of-the-art competitors when learning complex networks, where the independence assumption between blanket variables is clearly incorrect.
Fil: Schluter, Federico Enrique Adolfo. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Strappa Figueroa, Yanela Daiana. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional. Universidad Nacional del Litoral. Facultad de Ingeniería y Ciencias Hídricas. Instituto de Investigación en Señales, Sistemas e Inteligencia Computacional; Argentina
Fil: Bromberg, Facundo. Universidad Tecnológica Nacional. Facultad Regional de Mendoza; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina