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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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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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CoRR, 2023 Bayesian reinforcement learning (RL) offers a principled and elegant approach for sequential decision making under uncertainty. Most notably, Bayesian agents do not face an exploration/exploitation dilemma, a major pathology of frequentist methods. However theoretical understanding of model-free approaches is lacking.
Mattie Fellows +3 more
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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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Brain-Inspired Hardware Solutions for Inference in Bayesian Networks
Frontiers in Neuroscience, 2021 The implementation of inference (i.e., computing posterior probabilities) in Bayesian networks using a conventional computing paradigm turns out to be inefficient in terms of energy, time, and space, due to the substantial resources required by floating ...
Leila Bagheriye, Johan Kwisthout
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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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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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Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks
Sensors, 2021 We exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution ...
Federico Delussu +3 more
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On the Coherence of Probabilistic Relational Formalisms
Entropy, 2018 There are several formalisms that enhance Bayesian networks by including relations amongst individuals as modeling primitives. For instance, Probabilistic Relational Models (PRMs) use diagrams and relational databases to represent repetitive Bayesian ...
Glauber De Bona, Fabio G. Cozman
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