Results 11 to 20 of about 7,343,069 (347)

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   +10 more sources

Probabilistic Graphical Models [PDF]

Lecture Notes in Computer Science, 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.
S. Srihari
semanticscholar   +7 more sources

An Introduction to Probabilistic Graphical Models

Proceedings of the International Florida Artificial Intelligence Research Society Conference
This 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   +4 more sources

Mixed Nondeterministic-Probabilistic Automata: Blending graphical probabilistic models with nondeterminism [PDF]

, 2022
Graphical models in probability and statistics are a core concept in the area of probabilistic reasoning and probabilistic programming—graphical models include Bayesian networks and factor graphs.
Raclet, Jean-Baptiste, Benveniste, Albert   +1 more
core   +7 more sources

Recent advances in probabilistic graphical models [PDF]

Int. J. Intell. Syst., 2015
Probabilistic graphical models constitute a fundamental tool for the development of intelligent systems.
Concha Bielza, Serafín Moral, Antonio Salmerón   +2 more
openaire   +4 more sources

Probabilistic graphical models in astronomy [PDF]

Astronomy and Computing
The field of astronomy is experiencing a data explosion driven by significant advances in observational instrumentation, and classical methods often fall short of addressing the complexity of modern astronomical datasets. Probabilistic graphical models offer powerful tools for uncovering the dependence structures and data-generating processes ...
Abigail Sheerin, Giuseppe Vinci
  +6 more sources

How do probabilistic graphical models and graph neural networks look at network data?

Journal of Physics: Complexity
Graphs are a powerful data structure for representing relational data and are widely used to describe complex real-world systems. Probabilistic graphical models (PGMs) and graph neural networks (GNNs) can both leverage graph-structured data, but their ...
Michela Lapenna, Caterina De Bacco
doaj   +2 more sources

The probabilistic dependency patterns between interpersonal emotion regulation and prosocial behavior in Chinese adolescents: insights from Gaussian Graphical and Bayesian Network Models. [PDF]

BMC Psychol
Xiao M, Huang X, Yuan C, Xu J.
europepmc   +2 more sources

Probabilistic Graphical Models for Computational Biomedicine

Methods of Information in Medicine, 2003
Summary 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 ...
Y, Moreau, P, Antal, G, Fannes, B, De Moor   +3 more
openaire   +3 more sources

Perspectives on Probabilistic Graphical Models.

, 2020
QC ...
Liu, Dong
openaire   +3 more sources