Finding patterns in subsurface using Bayesian machine learning approach

Underground Space ◽

10.1016/j.undsp.2018.10.006 ◽

2020 ◽

Vol 5 (1) ◽

pp. 84-92 ◽

Author(s):

Hui Wang

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Machine Learning Approach ◽

Bayesian Machine Learning

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Mobile Collaborative Spectrum Sensing for Heterogeneous Networks: A Bayesian Machine Learning Approach

IEEE Transactions on Signal Processing ◽

10.1109/tsp.2018.2870379 ◽

2018 ◽

Vol 66 (21) ◽

pp. 5634-5647 ◽

Author(s):

Yizhen Xu ◽

Peng Cheng ◽

Zhuo Chen ◽

Yonghui Li ◽

Branka Vucetic

Keyword(s):

Machine Learning ◽

Spectrum Sensing ◽

Heterogeneous Networks ◽

Learning Approach ◽

Collaborative Spectrum Sensing ◽

Machine Learning Approach ◽

Bayesian Machine Learning

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A Bayesian Machine Learning Approach for Efficient Integrity Management of Steel Lazy Wave Risers

10.1115/1.0000856v ◽

2021 ◽

Author(s):

Seyed Rasoul Hejazi ◽

Andrew Grime ◽

Mark Randolph ◽

Mike Efthymiou

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Machine Learning Approach ◽

Integrity Management ◽

Bayesian Machine Learning

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A Bayesian Machine Learning Approach for Smart City

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39195 ◽

2021 ◽

Vol 9 (12) ◽

pp. 796-816

Author(s):

Ravi Prakash Malviya

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Machine Learning Approach ◽

Bayesian Machine Learning

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Bayesian Machine Learning Approach to the Quantification of Uncertainties on Ab Initio Potential Energy Surfaces

The Journal of Physical Chemistry A ◽

10.1021/acs.jpca.0c02395 ◽

2020 ◽

Vol 124 (25) ◽

pp. 5129-5146 ◽

Author(s):

S. Venturi ◽

R. L. Jaffe ◽

M. Panesi

Keyword(s):

Machine Learning ◽

Potential Energy ◽

Ab Initio ◽

Potential Energy Surfaces ◽

Learning Approach ◽

Machine Learning Approach ◽

Energy Surfaces ◽

Bayesian Machine Learning

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The Information Content of Forward-Looking Statements in Corporate Filings-A Naïve Bayesian Machine Learning Approach

Journal of Accounting Research ◽

10.1111/j.1475-679x.2010.00382.x ◽

2010 ◽

Vol 48 (5) ◽

pp. 1049-1102 ◽

Author(s):

FENG LI

Keyword(s):

Machine Learning ◽

Information Content ◽

Learning Approach ◽

Naive Bayesian ◽

Naïve Bayesian ◽

Machine Learning Approach ◽

Bayesian Machine Learning ◽

Forward Looking

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Neighborhood-level stroke and its neighborhood-level determinants: a Bayesian machine learning approach

ISEE Conference Abstracts ◽

10.1289/isee.2020.virtual.p-0168 ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

L. Hu ◽

B. Liu ◽

J. Ji ◽

Y. Li

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Machine Learning Approach ◽

Bayesian Machine Learning ◽

Neighborhood Level

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Identifying predictors of cancer prevalence at the neighborhood level in the United States: A Bayesian machine learning approach

ISEE Conference Abstracts ◽

10.1289/isee.2021.p-284 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Li Niu ◽

Liangyuan Hu ◽

Yan Li ◽

Bian Liu

Keyword(s):

United States ◽

Machine Learning ◽

The United States ◽

Learning Approach ◽

Cancer Prevalence ◽

Machine Learning Approach ◽

Bayesian Machine Learning ◽

Neighborhood Level

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A Bayesian machine learning approach to rapidly quantifying the fatigue probability of failure for steel catenary risers

Ocean Engineering ◽

10.1016/j.oceaneng.2021.109353 ◽

2021 ◽

Vol 235 ◽

pp. 109353

Author(s):

Rasoul Hejazi ◽

Andrew Grime ◽

Mark Randolph ◽

Mike Efthymiou

Keyword(s):

Machine Learning ◽

Probability Of Failure ◽

Learning Approach ◽

Machine Learning Approach ◽

Steel Catenary Risers ◽

Bayesian Machine Learning

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Accurate prediction of clinical disease progression in patients with advanced fibrosis due to NASH using a Bayesian machine learning approach

Journal of Hepatology ◽

10.1016/s0168-8278(18)31404-1 ◽

2018 ◽

Vol 68 ◽

pp. S573

Author(s):

J.C. Latourelle ◽

J. Tu ◽

R.K. Das ◽

L. Furchtgott ◽

B. Schoeberl ◽

...

Keyword(s):

Machine Learning ◽

Disease Progression ◽

Accurate Prediction ◽

Clinical Disease ◽

Learning Approach ◽

Advanced Fibrosis ◽

Machine Learning Approach ◽

Bayesian Machine Learning ◽

Clinical Disease Progression

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A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes

Journal of the American Statistical Association ◽

10.1080/01621459.2017.1340887 ◽

2018 ◽

Vol 113 (523) ◽

pp. 1255-1267 ◽

Author(s):

Thomas A. Murray ◽

Ying Yuan ◽

Peter F. Thall

Keyword(s):

Machine Learning ◽

Learning Approach ◽

Treatment Regimes ◽

Dynamic Treatment Regimes ◽

Machine Learning Approach ◽

Dynamic Treatment ◽

Bayesian Machine Learning

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