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A Survey on Bayesian Deep Learning
, 2020 A comprehensive artificial intelligence system needs to not only perceive the environment with different `senses' (e.g., seeing and hearing) but also infer the world's conditional (or even causal) relations and corresponding uncertainty.
Wang, Hao, Yeung, Dit-Yan
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Bayesian Models of Development
Trends in Ecology & Evolution, 2016 Until recently, biology lacked a framework for studying how information from genes, parental effects, and different personal experiences is combined across the lifetime to affect phenotypic development. Over the past few years, researchers have begun to build such a framework, using models that incorporate Bayesian updating to study the evolution of ...
Stamps, J.A., Frankenhuis, W.E.
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Bayesian Additive Regression Trees using Bayesian model averaging [PDF]
Statistics and Computing, 2017 Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for data sets where the number of variables $p$ is large (e.g.
Hernández, Belinda +3 more
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Bayesian Models for Unit Discovery on a Very Low Resource Language [PDF]
, 2018 Developing speech technologies for low-resource languages has become a very active research field over the last decade. Among others, Bayesian models have shown some promising results on artificial examples but still lack of in situ experiments. Our work
Besacier, Laurent +9 more
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Consensus clustering for Bayesian mixture models
BMC Bioinformatics, 2022 Background Cluster analysis is an integral part of precision medicine and systems biology, used to define groups of patients or biomolecules. Consensus clustering is an ensemble approach that is widely used in these areas, which combines the output from ...
Stephen Coleman +2 more
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Bayesian parametric models for survival prediction in medical applications
BMC Medical Research Methodology, 2023 Background Evidence-based treatment decisions in medicine are made founded on population-level evidence obtained during randomized clinical trials.
Iwan Paolucci +4 more
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Improving risk management for violence in mental health services: a multimethods approach
Programme Grants for Applied Research, 2016 Background: Mental health professionals increasingly carry out risk assessments to prevent future violence by their patients. However, there are problems with accuracy and these assessments do not always translate into successful risk management ...
Jeremy W Coid +21 more
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Bayesian hierarchical models and prior elicitation for fitting psychometric functions
Frontiers in Computational Neuroscience, 2023 Our previous articles demonstrated how to analyze psychophysical data from a group of participants using generalized linear mixed models (GLMM) and two-level methods.
Maura Mezzetti +7 more
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Bayesian inference for pulsar timing models [PDF]
, 2014 The extremely regular, periodic radio emission from millisecond pulsars makes them useful tools for studying neutron star astrophysics, general relativity, and low-frequency gravitational waves.
Vallisneri, Michele, Vigeland, Sarah J.
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Linear Bayesian Inference Theory Applied in Complex Analysis of Economic Forecasting and Management
Chemical Engineering Transactions, 2016 This paper systematically studied the Bayesian theory and methods of modern economic management in the multivariate models, including three linear models: single equation model, multi-equation model systems and Bayesian vector VAR (p) model.
C.Y. Bai
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