Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory
Entropy, 2020 Gaussian process emulators (GPE) are a machine learning approach that replicates computational demanding models using training runs of that model. Constructing such a surrogate is very challenging and, in the context of Bayesian inference, the training ...
Sergey Oladyshkin +3 more
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Neural surprise in somatosensory Bayesian learning.
PLoS Computational Biology, 2021 Tracking statistical regularities of the environment is important for shaping human behavior and perception. Evidence suggests that the brain learns environmental dependencies using Bayesian principles.
Sam Gijsen +4 more
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Shaky Student Growth? A Comparison of Robust Bayesian Learning Progress Estimation Methods
Journal of Intelligence, 2022 Monitoring the progress of student learning is an important part of teachers’ data-based decision making. One such tool that can equip teachers with information about students’ learning progress throughout the school year and thus facilitate monitoring ...
Boris Forthmann +2 more
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Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models [PDF]
, 2022 We develop an approach for Bayesian learning of spatiotemporal dynamical mechanistic models. Such learning consists of statistical emulation of the mechanistic system that can efficiently interpolate the output of the system from arbitrary inputs. The emulated learner can then be used to train the system from noisy data achieved by melding information ...
Banerjee, Sudipto +3 more
openaire +2 more sources
Individualization in Online Markets: A Generalized Model of Price Discrimination through Learning
Journal of Theoretical and Applied Electronic Commerce Research, 2023 This paper builds a theoretical framework to model individualization in online markets. In a market with consumers of varying levels of demand, a seller offers multiple product bundles and prices. Relative to brick-and-mortar stores, an online seller can
Rasha Ahmed
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Testing students' e-learning via Facebook through Bayesian structural equation modeling.
PLoS ONE, 2017 Learning is an intentional activity, with several factors affecting students' intention to use new learning technology. Researchers have investigated technology acceptance in different contexts by developing various theories/models and testing them by a ...
Hashem Salarzadeh Jenatabadi +4 more
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Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records
Scientific Reports, 2021 One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to model predictions.
Yikuan Li +8 more
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A comparison of machine learning and Bayesian modelling for molecular serotyping
BMC Genomics, 2017 Background Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease.
Richard Newton, Lorenz Wernisch
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Uncertainty-aware mixed-variable machine learning for materials design
Scientific Reports, 2022 Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the vast design space of chemistry, structure, and synthesis methods.
Hengrui Zhang +4 more
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Autonomous efficient experiment design for materials discovery with Bayesian model averaging [PDF]
PHYSICAL REVIEW MATERIALS, 2018 The accelerated exploration of the materials space in order to identify configurations with optimal properties is an ongoing challenge. Current paradigms are typically centered around the idea of performing this exploration through high-throughput ...
A. Talapatra +5 more
semanticscholar +1 more source