Results 1 to 10 of about 20,022 (282)
Getting started in probabilistic graphical models. [PDF]
PLoS Computational Biology, 2007 Probabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant?
Edoardo M Airoldi
doaj +11 more sources
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 +11 more sources
Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem [PDF]
Entropy, 2021 The goal of the Label Ranking (LR) problem is to learn preference models that predict the preferred ranking of class labels for a given unlabeled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In
Enrique G. Rodrigo +3 more
doaj +4 more sources
An Introduction to Probabilistic Graphical Models
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceThis tutorial covers an introduction to Probabilistic Graphical Models (PGM), such as Bayesian Networks and Markov Random Fields, for reasoning under uncertainty in intelligent systems.
Luigi Portinale
doaj +3 more sources
Teaching Probabilistic Graphical Models with OpenMarkov
Mathematics, 2022 OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries.
Francisco Javier Díez +3 more
doaj +2 more sources
Robust Depth Image Acquisition Using Modulated Pattern Projection and Probabilistic Graphical Models [PDF]
Sensors, 2016 Depth image acquisition with structured light approaches in outdoor environments is a challenging problem due to external factors, such as ambient sunlight, which commonly affect the acquisition procedure.
Jaka Kravanja +4 more
doaj +2 more sources
On a hypergraph probabilistic graphical model [PDF]
Annals of Mathematics and Artificial Intelligence, 2020 We propose a directed acyclic hypergraph framework for a probabilistic graphical model that we call Bayesian hypergraphs. The space of directed acyclic hypergraphs is much larger than the space of chain graphs. Hence Bayesian hypergraphs can model much finer factorizations than Bayesian networks or LWF chain graphs and provide simpler and more ...
Mohammad Ali Javidian +3 more
openaire +3 more sources
Strengthening Probabilistic Graphical Models: The Purge-and-Merge Algorithm
IEEE Access, 2021 Probabilistic graphical models (PGMs) are powerful tools for solving systems of complex relationships over a variety of probability distributions. However, while tree-structured PGMs always result in efficient and exact solutions, inference on graph (or ...
Simon Streicher, Johan A. Du Preez
doaj +1 more source
The Role of Instrumental Variables in Causal Inference Based on Independence of Cause and Mechanism
Entropy, 2021 Causal inference methods based on conditional independence construct Markov equivalent graphs and cannot be applied to bivariate cases. The approaches based on independence of cause and mechanism state, on the contrary, that causal discovery can be ...
Nataliya Sokolovska +1 more
doaj +1 more source
Probabilistic Graphical Models [PDF]
, 2015 In this chapter, we will briefly summarize the basic concepts of probability as well as graph theory.We start with important terms and definitions from graph theory and emphasize the relation to our hierarchical models. Directed and undirected graphs will be introduced and compared with each other. Then, we will give an overview of the random variables
+5 more sources