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Relevant bibliographies by topics / Bayesian Machine Learning (BML)

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Journal articles

Academic literature on the topic 'Bayesian Machine Learning (BML)'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Bayesian Machine Learning (BML)"

1

Rigueira, Xurxo, María Pazo, María Araújo, Saki Gerassis, and Elvira Bocos. "Bayesian Machine Learning and Functional Data Analysis as a Two-Fold Approach for the Study of Acid Mine Drainage Events." Water 15, no. 8 (2023): 1553. http://dx.doi.org/10.3390/w15081553.

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Acid mine drainage events have a negative influence on the water quality of fluvial systems affected by coal mining activities. This research focuses on the analysis of these events, revealing hidden correlations among potential factors that contribute to the occurrence of atypical measures and ultimately proposing the basis of an analytical tool capable of automatically capturing the overall behavior of the fluvial system. For this purpose, the hydrological and water quality data collected by an automated station located in a coal mining region in the NW of Spain (Fabero) were analyzed with a

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Mobiny, Aryan, Aditi Singh, and Hien Van Nguyen. "Risk-Aware Machine Learning Classifier for Skin Lesion Diagnosis." Journal of Clinical Medicine 8, no. 8 (2019): 1241. http://dx.doi.org/10.3390/jcm8081241.

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Knowing when a machine learning system is not confident about its prediction is crucial in medical domains where safety is critical. Ideally, a machine learning algorithm should make a prediction only when it is highly certain about its competency, and refer the case to physicians otherwise. In this paper, we investigate how Bayesian deep learning can improve the performance of the machine–physician team in the skin lesion classification task. We used the publicly available HAM10000 dataset, which includes samples from seven common skin lesion categories: Melanoma (MEL), Melanocytic Nevi (NV),

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3

Oladyshkin, Sergey, Farid Mohammadi, Ilja Kroeker, and Wolfgang Nowak. "Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory." Entropy 22, no. 8 (2020): 890. http://dx.doi.org/10.3390/e22080890.

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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 runs should be well invested. The current paper offers a fully Bayesian view on GPEs for Bayesian inference accompanied by Bayesian active learning (BAL). We introduce three BAL strategies that adaptively identify training sets for the GPE using information-theoretic arguments. The first strategy relies on Bayesian model evidence that indicates

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4

Zhou, Ting, Xiaohu Wen, Qi Feng, Haijiao Yu, and Haiyang Xi. "Bayesian Model Averaging Ensemble Approach for Multi-Time-Ahead Groundwater Level Prediction Combining the GRACE, GLEAM, and GLDAS Data in Arid Areas." Remote Sensing 15, no. 1 (2022): 188. http://dx.doi.org/10.3390/rs15010188.

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Accurate groundwater level (GWL) prediction is essential for the sustainable management of groundwater resources. However, the prediction of GWLs remains a challenge due to insufficient data and the complicated hydrogeological system. In this study, we investigated the ability of the Gravity Recovery and Climate Experiment (GRACE) satellite data, the Global Land Evaporation Amsterdam Model (GLEAM) data, the Global Land Data Assimilation System (GLDAS) data, and the publicly available meteorological data in 1-, 2-, and 3-month-ahead GWL prediction using three traditional machine learning models

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5

Kim, Sungwon, Meysam Alizamir, Nam Won Kim, and Ozgur Kisi. "Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series." Sustainability 12, no. 22 (2020): 9720. http://dx.doi.org/10.3390/su12229720.

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Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using

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Najafi, Mohammad Reza, Zahra Kavianpour, Banafsheh Najafi, Mohammad Reza Kavianpour, and Hamid Moradkhani. "Air demand in gated tunnels – a Bayesian approach to merge various predictions." Journal of Hydroinformatics 14, no. 1 (2011): 152–66. http://dx.doi.org/10.2166/hydro.2011.108.

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High flowrate through gated tunnels may cause critical flow conditions, especially downstream of the regulating gates. Aeration is found to be the most effective and efficient way to prevent cavitation attack. Several experimental equations are presented to predict air demand in gated tunnels; however, they are restricted to particular model geometries and flow conditions and often provide differing results. In this study the current relationships are first evaluated, and then other approaches for air discharge estimation are investigated. Three machine learning techniques are compared based o

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7

Xu, Ren, Nengcheng Chen, Yumin Chen, and Zeqiang Chen. "Downscaling and Projection of Multi-CMIP5 Precipitation Using Machine Learning Methods in the Upper Han River Basin." Advances in Meteorology 2020 (March 9, 2020): 1–17. http://dx.doi.org/10.1155/2020/8680436.

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Downscaling considerably alleviates the drawbacks of regional climate simulation by general circulation models (GCMs). However, little information is available regarding the downscaling using machine learning methods, specifically at hydrological basin scale. This study developed multiple machine learning (ML) downscaling models, based on a Bayesian model average (BMA), to downscale the precipitation simulation of 8 Coupled Model Intercomparison Project Phase 5 (CMIP5) models using model output statistics (MOS) for the years 1961–2005 in the upper Han River basin. A series of statistical metri

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Shu, Meiyan, Shuaipeng Fei, Bingyu Zhang, et al. "Application of UAV Multisensor Data and Ensemble Approach for High-Throughput Estimation of Maize Phenotyping Traits." Plant Phenomics 2022 (August 28, 2022): 1–17. http://dx.doi.org/10.34133/2022/9802585.

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High-throughput estimation of phenotypic traits from UAV (unmanned aerial vehicle) images is helpful to improve the screening efficiency of breeding maize. Accurately estimating phenotyping traits of breeding maize at plot scale helps to promote gene mining for specific traits and provides a guarantee for accelerating the breeding of superior varieties. Constructing an efficient and accurate estimation model is the key to the application of UAV-based multiple sensors data. This study aims to apply the ensemble learning model to improve the feasibility and accuracy of estimating maize phenotypi

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9

Quadeer, Ahmed A., Matthew R. McKay, John P. Barton, and Raymond H. Y. Louie. "MPF–BML: a standalone GUI-based package for maximum entropy model inference." Bioinformatics 36, no. 7 (2019): 2278–79. http://dx.doi.org/10.1093/bioinformatics/btz925.

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Abstract Summary Learning underlying correlation patterns in data is a central problem across scientific fields. Maximum entropy models present an important class of statistical approaches for addressing this problem. However, accurately and efficiently inferring model parameters are a major challenge, particularly for modern high-dimensional applications such as in biology, for which the number of parameters is enormous. Previously, we developed a statistical method, minimum probability flow–Boltzmann Machine Learning (MPF–BML), for performing fast and accurate inference of maximum entropy mo

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Soria-Olivas, E., J. Gomez-Sanchis, J. D. Martin, et al. "BELM: Bayesian Extreme Learning Machine." IEEE Transactions on Neural Networks 22, no. 3 (2011): 505–9. http://dx.doi.org/10.1109/tnn.2010.2103956.

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