Results 231 to 240 of about 152,362 (267)
Probabilistic graphical models [PDF]
Wiley Series in Probability and Statistics, 2014 This report presents probabilistic graphical models that are based on imprecise probabilities using a simplified language. In particular the discussion is focused on credal networks and discrete domains. It describes the building blocks of credal networks algorithms to perform inference and discusses on complexity results and related work.
Alessandro Antonucci +2 more
exaly +10 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Probabilistic Graphical Models
Advances in Computer Vision and Pattern Recognition, 2021The most important problem in machine learning is to estimate and infer the value of unknown variables (e.g., class label) based on the observed evidence (e.g., training samples). Probabilistic models provide a framework that considers learning problems as computing the probability distributions of variables.
L Enrique Sucar
exaly +3 more sources
Probabilistic Graphical Models of Dyslexia
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015Reading is a complex cognitive process, errors in which may assume diverse forms. In this study, introducing a novel approach, we use two families of probabilistic graphical models to analyze patterns of reading errors made by dyslexic people: an LDA-based model and two Naeve Bayes models which differ by their assumptions about the generation process ...
Yair Lakretz +3 more
openaire +1 more source
Survey of Probabilistic Graphical Models
2013 10th Web Information System and Application Conference, 2013Probabilistic graphical model (PGM) is a generic model that represents the probability-based relationships among random variables by a graph, and is a general method for knowledge representation and inference involving uncertainty. In recent years, PGM provides an important means for solving the uncertainty of intelligent information field, and becomes
Hongmei Li +3 more
openaire +1 more source
THE HUGIN TOOL FOR PROBABILISTIC GRAPHICAL MODELS
International Journal on Artificial Intelligence Tools, 2005As the framework of probabilistic graphical models becomes increasingly popular for knowledge representation and inference, the need for efficient tools for its support is increasing. The Hugin Tool is a general purpose tool for construction, maintenance, and deployment of Bayesian networks and influence diagrams.
Anders L. Madsen +3 more
openaire +2 more sources
Probabilistic reasoning with graphical security models
Information Sciences, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Barbara Kordy +2 more
openaire +3 more sources
Probabilistic Graphical Models for Computational Biomedicine
Methods of Information in Medicine, 2003Summary Background: As genomics becomes increasingly relevant to medicine, medical informatics and bioinformatics are gradually converging into a larger field that we call computational biomedicine. Objectives: Developing a computational framework that is common to the different disciplines that compose computational biomedicine ...
Bart De Moor
exaly +3 more sources
1|Functions on probabilistic graphical models
2009 International Multiconference on Computer Science and Information Technology, 2009Probabilistic graphical models are tools that are used to represent the probability distribution of a vector of random variables X = (X1, …, XN). In this paper we introduce functions f(x1, …, xN) defined over the given vector. These functions also are random variables.
Tomasz Ignac, Uli Sorger
openaire +1 more source