Results 261 to 270 of about 2,475,679 (305)

Constructing Site-Specific Multivariate Probability Distribution Model Using Bayesian Machine Learning

Journal of engineering mechanics, 2019
This study proposes a novel data-driven Bayesian machine learning method for constructing site-specific multivariate probability distribution models in geotechnical engineering.
J. Ching, K. Phoon
semanticscholar   +1 more source

Bayesian Transfer Learning Between Uniformly Modelled Bayesian Filters

2020
We investigate sensor network nodes that sequentially infer states with bounded values, and affected by noise that is also bounded. The transfer of knowledge between such nodes is the principal focus of this chapter. A fully Bayesian framework is adopted, in which the source knowledge is represented by a bounded data predictor, the specification of a ...
Ladislav Jirsa, Lenka Kuklišová Pavelková, Anthony Quinn   +2 more
openaire   +1 more source

Deep Bayesian Active Learning with Image Data

International Conference on Machine Learning, 2017
Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting.
Y. Gal, Riashat Islam, Zoubin Ghahramani
semanticscholar   +1 more source

Modelling Analysts’ Recommendations via Bayesian Machine Learning

SSRN Electronic Journal, 2018
We apply state-of-the-art Bayesian machine learning to test whether we can extract valuable information from analysts’ recommendations of stock performance. We use a probabilistic model for independent Bayesian classifier combination that has been successfully applied in both the physical and biological sciences. The technique is ideally suited for the
David Bew, Campbell R. Harvey, Anthony Ledford, Sam Radnor, Andrew Sinclair   +4 more
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Assigning a value to a power likelihood in a general Bayesian model

, 2017
Bayesian approaches to data analysis and machine learning are widespread and popular as they provide intuitive yet rigorous axioms for learning from data; see Bernardo and Smith (2004) and Bishop (2006).
C. Holmes, S. Walker
semanticscholar   +1 more source

Bayesian Modelling for Machine Learning

2005
Learning algorithms are central to pattern recognition, artificial intelligence, machine learning, data mining, and statistical learning. The term often implies analysis of large and complex data sets with minimal human intervention. Bayesian learning has been variously described as a method of updating opinion based on new experience, updating ...
Paul Rippon, Kerrie Mengersen
openaire   +1 more source

Learning tractable NAT-modeled Bayesian networks

Annals of Mathematics and Artificial Intelligence, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang Xiang, Qian Wang
openaire   +2 more sources

Bayesian learning of speech duration models

IEEE Transactions on Speech and Audio Processing, 2003
This paper presents the Bayesian speech duration modeling and learning for hidden Markov model (HMM) based speech recognition. We focus on the sequential learning of HMM state duration using quasi-Bayes (QB) estimate. The adapted duration models are robust to nonstationary speaking rates and noise conditions.
null Jen-Tzung Chien, null Chih-Hsien Huang   +1 more
openaire   +1 more source

Large Language Models to Enhance Bayesian Optimization

International Conference on Learning Representations
Bayesian optimization (BO) is a powerful approach for optimizing complex and expensive-to-evaluate black-box functions. Its importance is underscored in many applications, notably including hyperparameter tuning, but its efficacy depends on efficiently ...
Tennison Liu, Nicolás Astorga, Nabeel Seedat, M. Schaar   +3 more
semanticscholar   +1 more source

Recursive Bayesian Regression Modeling and Learning

2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007
This paper presents a new Bayesian regression and learning algorithm for adaptive pattern classification. Our aim is to continuously update regression parameters to meet nonstationary environments for real-world applications. Here, a kernel regression model is used to represent two-class data.
Jen-Tzung Chien, Jung-Chun Chen
openaire   +1 more source