A Conceptual Introduction to Bayesian Model Averaging
Advances in Methods and Practices in Psychological Science, 2019 Many statistical scenarios initially involve several candidate models that describe the data-generating process. Analysis often proceeds by first selecting the best model according to some criterion and then learning about the parameters of this selected
M. Hinne +3 more
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Learning oncogenetic networks by reducing to mixed integer linear programming. [PDF]
PLoS ONE, 2013 Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events.
Hossein Shahrabi Farahani +1 more
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Second-Order Belief Hidden Markov Models [PDF]
, 2014 Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model.
A. Aregui +17 more
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On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events [PDF]
npj Computational Materials, 2019 Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented ...
Jonathan Vandermause +6 more
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STEM-Based Bayesian Computational Learning Model-BCLM for Effective Learning of Bayesian Statistics
IEEE AccessThis work contributes to the comprehension of Bayes’ theorem inclusive Bayesian probabilities and Bayesian inferencing within the framework of STEM (Science, Technology, Engineering, Arts, and Mathematics) and cognitive learning w.r.t Bloom’
Ikram E. Khuda +2 more
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Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
, 2018 This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted ...
Damianou, Andreas +3 more
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Bayesian Learning and Predictability in a Stochastic Nonlinear Dynamical Model [PDF]
, 2012 Bayesian inference methods are applied within a Bayesian hierarchical modelling framework to the problems of joint state and parameter estimation, and of state forecasting.
Campbell, Edward P. +4 more
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A nonparametric Bayesian approach toward robot learning by demonstration
, 2012 In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose.
Antoniak +35 more
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LSTM Learning With Bayesian and Gaussian Processing for Anomaly Detection in Industrial IoT
IEEE Transactions on Industrial Informatics, 2020 The data generated by millions of sensors in the industrial Internet of Things (IIoT) are extremely dynamic, heterogeneous, and large scale and pose great challenges on the real-time analysis and decision making for anomaly detection in the IIoT. In this
Di Wu +5 more
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Bayesian Model-Agnostic Meta-Learning
, 2018 First two authors contributed equally. 15 pages with appendix including experimental details.
Kim, Taesup +5 more
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