Results 11 to 20 of about 394,931 (262)
PLoS Computational Biology, 2021 In this work, we introduce an entirely data-driven and automated approach to reveal disease-associated biomarker and risk factor networks from heterogeneous and high-dimensional healthcare data.
Ann-Kristin Becker +9 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 Quantum Neural Networks
IEEE Access, 2022 The astounding acceleration in Artificial Intelligence and Quantum Computing advances naturally gives rise to a line of research, which unrolls the potential advantages of quantum computing on classical Machine Learning tasks, known as Quantum Machine ...
Nam Nguyen, Kwang-Cheng Chen
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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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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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Random matrix analysis for gene interaction networks in cancer cells [PDF]
, 2018 Investigations of topological uniqueness of gene interaction networks in cancer cells are essential for understanding this disease. Based on the random matrix theory, we study the distribution of the nearest neighbor level spacings $P(s)$ of interaction ...
Kikkawa, Ayumi
core +3 more sources
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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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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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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