DeepAI AI Chat

From Incomplete, Dynamic Data to Bayesian Networks

06/15/2019

∙

by Marco Scutari, et al.

∙

IDSIA

∙

∙

Bayesian networks are a versatile and powerful tool to model complex phenomena and the interplay of their components in a probabilistically principled way. Moving beyond the comparatively simple case of completely observed, static data, which has received the most attention in the literature, in this paper we will review how Bayesian networks can model dynamic data and data with incomplete observations. Such data are the norm at the forefront of research and applications, and Bayesian networks are uniquely positioned to model them due to their explainability and interpretability.

∙ 05/12/2022

Comments on: "Hybrid Semiparametric Bayesian Networks"

Invited discussion on the paper "Hybrid Semiparametric Bayesian Networks...

0 Marco Scutari, et al. ∙

∙ 07/04/2017

Visualizing the Consequences of Evidence in Bayesian Networks

This paper addresses the challenge of viewing and navigating Bayesian ne...

0 Clifford Champion, et al. ∙

∙ 02/14/2012

EDML: A Method for Learning Parameters in Bayesian Networks

We propose a method called EDML for learning MAP parameters in binary Ba...

0 Arthur Choi, et al. ∙

∙ 08/07/2014

Updating with incomplete observations

Currently, there is renewed interest in the problem, raised by Shafer in...

0 Gert de Cooman, et al. ∙

∙ 09/15/2023

Virtual Harassment, Real Understanding: Using a Serious Game and Bayesian Networks to Study Cyberbullying

Cyberbullying among minors is a pressing concern in our digital society,...

0 jaime perez, et al. ∙

∙ 07/15/2019

A Causal Bayesian Networks Viewpoint on Fairness

We offer a graphical interpretation of unfairness in a dataset as the pr...

0 Silvia Chiappa, et al. ∙

∙ 09/26/2013

Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks

Exact algorithms for learning Bayesian networks guarantee to find provab...

0 Brandon Malone, et al. ∙