Results 11 to 20 of about 399,768 (265)
Bayesian Networks in Radiology. [PDF]
Radiol Artif Intell, 2023 A Bayesian network is a graphical model that uses probability theory to represent relationships among its variables. The model is a directed acyclic graph whose nodes represent variables, such as the presence of a disease or an imaging finding. Connections between nodes express causal influences between variables as probability values.
Ma SX +6 more
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Bayesian Approach to Linear Bayesian Networks
CoRR, 2023 This study proposes the first Bayesian approach for learning high-dimensional linear Bayesian networks. The proposed approach iteratively estimates each element of the topological ordering from backward and its parent using the inverse of a partial covariance matrix.
Seyong Hwang +3 more
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Bayesian Network Classifiers [PDF]
Machine Learning, 1997 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Nir Friedman +2 more
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Bayesian Neural Networks [PDF]
, 2022 In recent times, neural networks have become a powerful tool for the analysis of complex and abstract data models. However, their introduction intrinsically increases our uncertainty about which features of the analysis are model-related and which are due to the neural network.
Tom Charnock +2 more
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CoRR, 2023 This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, then passed as input to a neural network that outputs a second, interdependent distribution.
Alex Graves +3 more
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Probabilistic Graph Models (PGMs) for Feature Selection in Time Series Analysis and Forecasting
JISR on Computing, 2021 Time series or longitudinal analysis has a very important aspect in the field of research. Day by day new and better analyses are getting developed in this field.
Syed Ali Raza Naqvi
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Mathematical and Computational Applications, 2020 We present a novel bi-objective approach to address the data-driven learning problem of Bayesian networks. Both the log-likelihood and the complexity of each candidate Bayesian network are considered as objectives to be optimized by our proposed ...
Vicente-Josué Aguilera-Rueda +2 more
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Feature Dynamic Bayesian Networks [PDF]
, 2008 Feature Markov Decision Processes (PhiMDPs) are well-suited for learning agents in general environments. Nevertheless, unstructured (Phi)MDPs are limited to relatively simple environments.
Hutter, Marcus
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High-Dimensional Bayesian Network Inference From Systems Genetics Data Using Genetic Node Ordering
Frontiers in Genetics, 2019 Studying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait
Lingfei Wang +6 more
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A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks
International Journal of Interactive Multimedia and Artificial Intelligence, 2014 Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that ...
Sho Fukuda +2 more
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