Results 31 to 40 of about 6,628,841 (378)
Discrete Dynamics in Nature and Society, 2014 This paper presents a Bayesian network model for estimating origin-destination matrices. Most existing Bayesian methods adopt prior OD matrixes, which are always troublesome to be obtained.
Lin Cheng +3 more
doaj +1 more source
Semiparametric Bayesian Networks [PDF]
arXiv, 2021 We introduce semiparametric Bayesian networks that combine parametric and nonparametric conditional probability distributions. Their aim is to incorporate the advantages of both components: the bounded complexity of parametric models and the flexibility of nonparametric ones.
arxiv
Compiling Bayesian Network Classifiers into Decision Graphs
AAAI Conference on Artificial Intelligence, 2019 We propose an algorithm for compiling Bayesian network classifiers into decision graphs that mimic the input and output behavior of the classifiers. In particular, we compile Bayesian network classifiers into ordered decision graphs, which are tractable ...
Andy Shih, Arthur Choi, Adnan Darwiche
semanticscholar +1 more source
Bayesian networks in neuroscience: a survey [PDF]
Frontiers in Computational Neuroscience, 2014 Bayesian networks are a type of probabilistic graphical models lie at the intersection between statistics and machine learning. They have been shown to be powerful tools to encode dependence relationships among the variables of a domain under uncertainty.
Pedro Larrañaga, Concha Bielza
openaire +4 more sources
Twin neural network regression
Applied AI Letters, Volume 3, Issue 4, December 2022., 2022 We propose to reformulate a regression problem into predicting differences between target values. This allows for leveraging consistency conditions which can be used as uncertainty estimates and enable the production of an ensemble of predictions while training only a single neural network.
Sebastian Johann Wetzel +3 more
wiley +1 more source
Grid Quality of Service Trustworthiness Evaluation Based on Bayesian Network
IEEE Access, 2020 Quality of Service (QoS) is applied to evaluate the satisfaction level of users using a service and it is a measure and evaluation of the service level of service providers.
Yiling Huang
doaj +1 more source
IEEE Transactions on Industrial Informatics, 2018 With the increasing deployment of data network technologies in industrial control systems (ICSs), cybersecurity becomes a challenging problem in ICSs. Dynamic cybersecurity risk assessment plays a vital role in ICS cybersecurity protection.
Qi Zhang +5 more
semanticscholar +1 more source
Monotonicity in Bayesian Networks
, 2004 Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)
Hans L. Bodlaender +2 more
openaire +5 more sources
Bayesian generalized network design [PDF]
Theoretical Computer Science, 2020 25 pages, 0 figure. An extended abstract of this paper is to appear in the 27th Annual European Symposium on Algorithms (ESA 2019)
Yuval Emek +3 more
openaire +5 more sources
Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers
Complexity, 2018 Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization.
Gonzalo A. Ruz, Pamela Araya-Díaz
doaj +1 more source