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

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Author: Grafiati

Published: 6 September 2023

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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 (April 15, 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 advanced mathematical methods: statistical Bayesian machine learning (BML) and functional data analysis (FDA). The Bayesian analysis describes a structure fully dedicated to explaining the behavior of the fluvial system and the characterization of the pH, delving into its statistical association with the rest of the variables in the model. FDA allows the definition of several time-dependent correlations between the functional outliers of different variables, namely, the inverse relationship between pH, rainfall, and flow. The results demonstrate that an analytical tool structured around a Bayesian model and functional analysis automatically captures different patterns of the pH in the fluvial system and identifies the underlying anomalies.

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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 (August 17, 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), Basal Cell Carcinoma (BCC), Actinic Keratoses and Intraepithelial Carcinoma (AKIEC), Benign Keratosis (BKL), Dermatofibroma (DF), and Vascular (VASC) lesions. Our experimental results show that Bayesian deep networks can boost the diagnostic performance of the standard DenseNet-169 model from 81.35% to 83.59% without incurring additional parameters or heavy computation. More importantly, a hybrid physician–machine workflow reaches a classification accuracy of 90 % while only referring 35 % of the cases to physicians. The findings are expected to generalize to other medical diagnosis applications. We believe that the availability of risk-aware machine learning methods will enable a wider adoption of machine learning technology in clinical settings.

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Oladyshkin, Sergey, Farid Mohammadi, Ilja Kroeker, and Wolfgang Nowak. "Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory." Entropy 22, no. 8 (August 13, 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 the GPE’s quality of matching the measurement data, the second strategy is based on relative entropy that indicates the relative information gain for the GPE, and the third is founded on information entropy that indicates the missing information in the GPE. We illustrate the performance of our three strategies using analytical- and carbon-dioxide benchmarks. The paper shows evidence of convergence against a reference solution and demonstrates quantification of post-calibration uncertainty by comparing the introduced three strategies. We conclude that Bayesian model evidence-based and relative entropy-based strategies outperform the entropy-based strategy because the latter can be misleading during the BAL. The relative entropy-based strategy demonstrates superior performance to the Bayesian model evidence-based strategy.

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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 (December 29, 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 (extreme learning machine, ELM; support vector machine, SVR; and random forest, RF). Meanwhile, we further developed the Bayesian model averaging (BMA) by combining the ELM, SVR, and RF models to avoid the uncertainty of the single models and to improve the predicting accuracy. The validity of the forcing data and the BMA model were assessed for three GWL monitoring wells in the Zhangye Basin in Northwest China. The results indicated that the applied forcing data could be treated as validated inputs to predict the GWL up to 3 months ahead due to the achieved high accuracy of the machine learning models (NS > 0.55). The BMA model could significantly improve the performance of the single machine learning models. Overall, the BMA model reduced the RMSE of the ELM, SVR, and RF models in the testing period by about 13.75%, 24.01%, and 17.69%, respectively; while it improved the NS by about 8.32%, 16.13%, and 9.67% for 1-, 2-, and 3-month-ahead GWL prediction, respectively. The uncertainty analysis results also verified the reliability of the BMA model in multi-time-ahead GWL predicting. This highlighted the efficiency of the satellite data, satellite-based data, and publicly available data as substitute inputs in machine-learning-based GWL prediction, particularly for areas with insufficient or missing data. Meanwhile, the BMA ensemble strategy can serve as a powerful and reliable approach in multi-time-ahead GWL prediction when risk-based decision making is needed or a lack of relevant hydrogeological data impedes the application of the physical models.

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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 (November 21, 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 different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.

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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 (April 23, 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 on the flow measurements of eight physical models, with scales ranging from 1:12–1:20, including the fuzzy inference system (FIS), the genetic fuzzy system (GFS), and the adaptive network-based fuzzy inference system (ANFIS). The Bayesian Model Average (BMA) method is then proposed as a tool to merge the simulations from all models. The BMA provides the weighted average of the predictions, by assigning weights to each model in a probabilistic approach. The application of the BMA is found to be useful for improving the design of hydraulic structures by combining different models and experimental equations.

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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 metrics, including Pearson’s correlation coefficient (PCC), root mean squared error (RMSE), and relative bias (Rbias), were used for evaluation and comparative analyses. Moreover, the BMA and the best ML downscaling model were used to downscale precipitation in the 21st century under Representative Concentration Pathway 4.5 (RCP4.5) and RCP8.5 scenarios. The results show the following: (1) The performance of the BMA ensemble simulation is clearly better than that of the individual models and the simple mean model ensemble (MME). The PCC reaches 0.74, and the RMSE is reduced by 28%–60% for all the GCMs and 33% compared to the MME. (2) The downscaled models greatly improved station simulation performance. Support vector machine for regression (SVR) was superior to multilayer perceptron (MLP) and random forest (RF). The downscaling results based on the BMA ensemble simulation and SVR models were regarded as the best performing overall (PCC, RMSE, and Rbias were 0.82, 35.07, mm and −5.45%, respectively). (3) Based on BMA and SVR models, the projected precipitations show a weak increasing trend on the whole under RCP4.5 and RCP8.5. Specifically, the average rainfall during the mid- (2040–2069) and late (2070–2099) 21st century increased by 3.23% and 1.02%, respectively, compared to the base year (1971–2000) under RCP4.5, while they increased by 4.25% and 8.30% under RCP8.5. Additionally, the magnitude of changes during winter and spring was higher than that during summer and autumn. Furthermore, future work is recommended to study the improvement of downscaling models and the effect of local climate.

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Shu, Meiyan, Shuaipeng Fei, Bingyu Zhang, Xiaohong Yang, Yan Guo, Baoguo Li, and Yuntao Ma. "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 phenotypic traits using UAV-based red-green-blue (RGB) and multispectral sensors. The UAV images of four growth stages were obtained, respectively. The reflectance of visible light bands, canopy coverage, plant height (PH), and texture information were extracted from RGB images, and the vegetation indices were calculated from multispectral images. We compared and analyzed the estimation accuracy of single-type feature and multiple features for LAI (leaf area index), fresh weight (FW), and dry weight (DW) of maize. The basic models included ridge regression (RR), support vector machine (SVM), random forest (RF), Gaussian process (GP), and K-neighbor network (K-NN). The ensemble learning models included stacking and Bayesian model averaging (BMA). The results showed that the ensemble learning model improved the accuracy and stability of maize phenotypic traits estimation. Among the features extracted from UAV RGB images, the highest accuracy was obtained by the combination of spectrum, structure, and texture features. The model had the best accuracy constructed using all features of two sensors. The estimation accuracies of ensemble learning models, including stacking and BMA, were higher than those of the basic models. The coefficient of determination (R2) of the optimal validation results were 0.852, 0.888, and 0.929 for LAI, FW, and DW, respectively. Therefore, the combination of UAV-based multisource data and ensemble learning model could accurately estimate phenotyping traits of breeding maize at plot scale.

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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 (December 18, 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 model parameters, which was applied to genetic sequence data to estimate the fitness landscape for the surface proteins of human immunodeficiency virus and hepatitis C virus. To facilitate seamless use of MPF–BML and encourage more widespread application to data in diverse fields, we present a standalone cross-platform package of MPF–BML which features an easy-to-use graphical user interface. The package only requires the input data (protein sequence data or data of multiple configurations of a complex system with large number of variables) and returns the maximum entropy model parameters. Availability and implementation The MPF–BML software is publicly available under the MIT License at https://github.com/ahmedaq/MPF-BML-GUI. Supplementary information Supplementary data are available at Bioinformatics online.

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Soria-Olivas, E., J. Gomez-Sanchis, J. D. Martin, J. Vila-Frances, M. Martinez, J. R. Magdalena, and A. J. Serrano. "BELM: Bayesian Extreme Learning Machine." IEEE Transactions on Neural Networks 22, no. 3 (March 2011): 505–9. http://dx.doi.org/10.1109/tnn.2010.2103956.

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11

Biletskyy, B. "Distributed Bayesian Machine Learning Procedures." Cybernetics and Systems Analysis 55, no. 3 (May 2019): 456–61. http://dx.doi.org/10.1007/s10559-019-00153-4.

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12

Chen, Yarui, Jucheng Yang, Chao Wang, and DongSun Park. "Variational Bayesian extreme learning machine." Neural Computing and Applications 27, no. 1 (September 24, 2014): 185–96. http://dx.doi.org/10.1007/s00521-014-1710-1.

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13

Suyama, Atsushi. "Introduction to Bayesian Machine Learning." Journal of the Robotics Society of Japan 40, no. 10 (2022): 857–62. http://dx.doi.org/10.7210/jrsj.40.857.

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Li, Yifen, Yun Wang, Zhiya Chen, and Runmin Zou. "Bayesian robust multi-extreme learning machine." Knowledge-Based Systems 210 (December 2020): 106468. http://dx.doi.org/10.1016/j.knosys.2020.106468.

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Gandhi, Shipra, Sarabjot Pabla, Mary Nesline, Manu Pandey, Marc S. Ernstoff, Grace K. Dy, Jeffery M. Conroy, et al. "Algorithmic prediction of response to checkpoint inhibitors: Hyperprogressors versus responders." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 11565. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.11565.

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11565 Background: Predicting response to checkpoint inhibitors (CPIs) using biological knowledge-based decision processes with machine learning (ML) has a great potential to predict rapid progression in patients treated with checkpoint inhibitors (CPIs) (hyperprogressive disease (HPD)) as well as responders. ML models risk overfitting data and do not always evaluate the underlying biology, thus performing well in the initial training cohort but lack generalizability when extended to other cohorts. Biology-based decision may not perform as well initially due to limited understanding and a simplified rule set, but often perform equally well when extended to larger similar cohorts of patients. Methods: A custom NGS cancer immune gene expression assay compared 87 patients treated with CPIs classified as CR, PR, or SD versus 12 HPD. A ML-based polynomial regression model based on 54 immune-related genes combined with mutational burden was optimized for prediction of response. A biological 4-gene decision tree model was constructed independently based on ML. A second biological decision tree incorporated the weighted average relative rank of the expression of multiple genes in 4 different immune functions including immune cell infiltration, regulation, activation, and cytokine signaling. Bayesian model average (BMA) incorporated all three models’ results into the final prediction. Results: For87 patients classified as CR, PR, or SD the PPV >96% for responders and a NPV >90% for non-responders was achieved with the regression model, however with response indeterminate for 24% of the population. While the two biological decision tree models’ PPV were in the 70% range, they accurately revealed the critical genes’ roles in immune response with strong literature support. BMA process integrated these three models resulted in a PPV >96% and a NPV >90% and eliminated the indeterminate group. For HPD a unique biology related to priming of short term memory T-cells was identified. Conclusion: Prediction of response to CPIs is best attained by combining ML with biological knowledge. Decision tree models using a large panel of immune related genes in the context of archival samples from patients treated with CPIs can be used to better understand the biology of responders versus non-responders and provides new insights into HPD.

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Wang, Peipei, Xinqi Zheng, Junhua Ku, and Chunning Wang. "Multiple-Instance Learning Approach via Bayesian Extreme Learning Machine." IEEE Access 8 (2020): 62458–70. http://dx.doi.org/10.1109/access.2020.2984271.

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Wai Lam. "Bayesian network refinement via machine learning approach." IEEE Transactions on Pattern Analysis and Machine Intelligence 20, no. 3 (March 1998): 240–51. http://dx.doi.org/10.1109/34.667882.

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Krems, R. V. "Bayesian machine learning for quantum molecular dynamics." Physical Chemistry Chemical Physics 21, no. 25 (2019): 13392–410. http://dx.doi.org/10.1039/c9cp01883b.

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Karandikar, Jaydeep, Andrew Honeycutt, Scott Smith, and Tony Schmitz. "Milling stability identification using Bayesian machine learning." Procedia CIRP 93 (2020): 1423–28. http://dx.doi.org/10.1016/j.procir.2020.04.022.

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Bew, David, Campbell R. Harvey, Anthony Ledford, Sam Radnor, and Andrew Sinclair. "Modeling Analysts’ Recommendations via Bayesian Machine Learning." Journal of Financial Data Science 1, no. 1 (January 31, 2019): 75–98. http://dx.doi.org/10.3905/jfds.2019.1.1.075.

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Zhu, Jun, Jianfei Chen, Wenbo Hu, and Bo Zhang. "Big Learning with Bayesian methods." National Science Review 4, no. 4 (May 4, 2017): 627–51. http://dx.doi.org/10.1093/nsr/nwx044.

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AbstractThe explosive growth in data volume and the availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems and applications with Big Data. Bayesian methods represent one important class of statistical methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including non-parametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications. We also provide various new perspectives on the large-scale Bayesian modeling and inference.

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Boyko, Nataliya, and Oleksandra Dypko. "Analysis of Machine Learning Methods Using Spam Filtering." Modeling Control and Information Technologies, no. 5 (November 21, 2021): 25–28. http://dx.doi.org/10.31713/mcit.2021.06.

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The paper considers methods of the naive Bayesian classifier. Experiments that show independence between traits are described. Describes the naive Bayesian classifier used to filter spam in messages. The aim of the study is to determine the best method to solve the problem of spam in messages. The paper considers three different variations of the naive Bayesian classifier. The results of experiments and research are given.

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J, Dr Visumathi, Tetala Durga Venkata Rama Reddy, Velagapudi Abhinandhan, and Panamganti Anil Kumar. "Multi-Disease Prediction Using Machine Learning Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 447–53. http://dx.doi.org/10.22214/ijraset.2023.50128.

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Abstract: In the medical sector, disease diagnosis is an essential duty, and prompt and accurate diagnosis is crucial to effective management and therapy. Machine learning techniques, including Naive Bayesian networks, have shown promise in disease prediction and diagnosis. In this study, we present a machine learning-based multi-disease prediction system that uses Naive Bayesian networks. The proposed methodology seeks to deliver precise illness prediction for several diseases instantaneously. In addition to describing the methods adopted, which included dataset selection, preprocessing, feature selection, and the Naive Bayesian network algorithm, we also discuss the social relevance of this work, emphasizing the potential impact of accurate disease prediction in improving patient outcomes and bringing down healthcare costs. To evaluate the performance of the proposed model, we conducted experiments using a publicly available disease dataset. The results demonstrated that the proposed model achieved high accuracy of 91.2% and outperformed other state-of-the-art models for multi-disease prediction some of them are, Random Forest obtained 85.7% and Decision Tree obtained 81.3% respectively. In summary, the proposed system demonstrates the effectiveness of Naive Bayesian networks for multi-disease prediction and has the potential to improve disease diagnosis and management in the medical domain

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Tresp, Volker. "A Bayesian Committee Machine." Neural Computation 12, no. 11 (November 1, 2000): 2719–41. http://dx.doi.org/10.1162/089976600300014908.

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The Bayesian committee machine (BCM) is a novel approach to combining estimators that were trained on different data sets. Although the BCM can be applied to the combination of any kind of estimators, the main foci are gaussian process regression and related systems such as regularization networks and smoothing splines for which the degrees of freedom increase with the number of training data. Somewhat surprisingly, we find that the performance of the BCM improves if several test points are queried at the same time and is optimal if the number of test points is at least as large as the degrees of freedom of the estimator. The BCM also provides a new solution for on-line learning with potential applications to data mining. We apply the BCM to systems with fixed basis functions and discuss its relationship to gaussian process regression. Finally, we show how the ideas behind the BCM can be applied in a non-Bayesian setting to extend the input-dependent combination of estimators.

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Geer, A. J. "Learning earth system models from observations: machine learning or data assimilation?" Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, no. 2194 (February 15, 2021): 20200089. http://dx.doi.org/10.1098/rsta.2020.0089.

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Recent progress in machine learning (ML) inspires the idea of improving (or learning) earth system models directly from the observations. Earth sciences already use data assimilation (DA), which underpins decades of progress in weather forecasting. DA and ML have many similarities: they are both inverse methods that can be united under a Bayesian (probabilistic) framework. ML could benefit from approaches used in DA, which has evolved to deal with real observations—these are uncertain, sparsely sampled, and only indirectly sensitive to the processes of interest. DA could also become more like ML and start learning improved models of the earth system, using parameter estimation, or by directly incorporating machine-learnable models. DA follows the Bayesian approach more exactly in terms of representing uncertainty, and in retaining existing physical knowledge, which helps to better constrain the learnt aspects of models. This article makes equivalences between DA and ML in the unifying framework of Bayesian networks. These help illustrate the equivalences between four-dimensional variational (4D-Var) DA and a recurrent neural network (RNN), for example. More broadly, Bayesian networks are graphical representations of the knowledge and processes embodied in earth system models, giving a framework for organising modelling components and knowledge, whether coming from physical equations or learnt from observations. Their full Bayesian solution is not computationally feasible but these networks can be solved with approximate methods already used in DA and ML, so they could provide a practical framework for the unification of the two. Development of all these approaches could address the grand challenge of making better use of observations to improve physical models of earth system processes. This article is part of the theme issue ‘Machine learning for weather and climate modelling’.

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Sohail, Ayesha. "INFERENCE OF BIOMEDICAL DATA SETS USING BAYESIAN MACHINE LEARNING." Biomedical Engineering: Applications, Basis and Communications 31, no. 04 (June 27, 2019): 1950030. http://dx.doi.org/10.4015/s1016237219500303.

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Due to the advancement in data collection and maintenance strategies, the current clinical databases around the globe are rich in a sense that these contain detailed information not only about the individual’s medical conditions, but also about the environmental features, associated with the individual. Classification within this data could provide new medical insights. Data mining technology has become an attraction for researchers due to its affectivity and efficacy in the field of biomedicine research. Due to the diverse structure of such data sets, only few successful techniques and easy to use softwares, are available in literature. A Bayesian analysis provides a more intuitive statement of probability that hypothesis is true. Bayesian approach uses all available information and can give answers to complex questions more accurately. This means that Bayesian methods include prior information. In Bayesian analysis, no relevant information is excluded as prior represents all the available information apart from data itself. Bayesian techniques are specifically used for decision making. Uncertainty is the main hurdle in making decisions. Due to lack of information about relevant parameters, there is uncertainty about given decision. Bayesian methods measure these uncertainties by using probability. In this study, selected techniques of biostatistical Bayesian inference (the probability based inferencing approach, to identify uncertainty in databases) are discussed. To show the efficiency of a Hybrid technique, its application on two distinct data sets is presented in a novel way.

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Malviya, Ravi Prakash. "A Bayesian Machine Learning Approach for Smart City." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 796–816. http://dx.doi.org/10.22214/ijraset.2021.39195.

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Gao, Haiping, Shifa Zhong, Wenlong Zhang, Thomas Igou, Eli Berger, Elliot Reid, Yangying Zhao, et al. "Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization." Environmental Science & Technology 56, no. 4 (December 30, 2021): 2572–81. http://dx.doi.org/10.1021/acs.est.1c04373.

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Jun, Sunghae, #VALUE! #VALUE!, and #VALUE! #VALUE! "Regression Machine Learning using Bayesian Inference and Regularization." Journal of Korean Institute of Intelligent Systems 29, no. 5 (October 31, 2019): 390–94. http://dx.doi.org/10.5391/jkiis.2019.29.5.390.

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Wu, Wei, Srikantan Nagarajan, and Zhe Chen. "Bayesian Machine Learning: EEG\/MEG signal processing measurements." IEEE Signal Processing Magazine 33, no. 1 (January 2016): 14–36. http://dx.doi.org/10.1109/msp.2015.2481559.

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Chakraborty, Sounak. "Bayesian semi-supervised learning with support vector machine." Statistical Methodology 8, no. 1 (January 2011): 68–82. http://dx.doi.org/10.1016/j.stamet.2009.09.002.

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Sarkar, Dripta, Michael A. Osborne, and Thomas A. A. Adcock. "Prediction of tidal currents using Bayesian machine learning." Ocean Engineering 158 (June 2018): 221–31. http://dx.doi.org/10.1016/j.oceaneng.2018.03.007.

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Wang, Jing, Lin Zhang, Juan-juan Cao, and Di Han. "NBWELM: naive Bayesian based weighted extreme learning machine." International Journal of Machine Learning and Cybernetics 9, no. 1 (December 27, 2014): 21–35. http://dx.doi.org/10.1007/s13042-014-0318-1.

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Jiahua Luo, Chi-Man Vong, and Pak-Kin Wong. "Sparse Bayesian Extreme Learning Machine for Multi-classification." IEEE Transactions on Neural Networks and Learning Systems 25, no. 4 (April 2014): 836–43. http://dx.doi.org/10.1109/tnnls.2013.2281839.

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Song, Min-Jong, and Yong-Sik Cho. "Probabilistic Tsunami Heights Model using Bayesian Machine Learning." Journal of Coastal Research 95, sp1 (May 26, 2020): 1291. http://dx.doi.org/10.2112/si95-249.1.

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Hobson, Michael, Philip Graff, Farhan Feroz, and Anthony Lasenby. "Machine-learning in astronomy." Proceedings of the International Astronomical Union 10, S306 (May 2014): 279–87. http://dx.doi.org/10.1017/s1743921314013672.

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AbstractMachine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, calledSkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. TheSkyNetand BAMBI packages, which are fully parallelised using MPI, are available athttp://www.mrao.cam.ac.uk/software/.

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White, Brian S., Suleiman A. Khan, Muhammad Ammad-ud-din, Swapnil Potdar, Mike J. Mason, Cristina E. Tognon, Brian J. Druker, et al. "Comparative Analysis of Independent Ex Vivo functional Drug Screens Identifies Predictive Biomarkers of BCL-2 Inhibitor Response in AML." Blood 132, Supplement 1 (November 29, 2018): 2763. http://dx.doi.org/10.1182/blood-2018-99-111916.

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Abstract Introduction: Therapeutic options for patients with AML were recently expanded with FDA approval of four drugs in 2017. As their efficacy is limited in some patient subpopulations and relapse ultimately ensues, there remains an urgent need for additional treatment options tailored to well-defined patient subpopulations to achieve durable responses. Two comprehensive profiling efforts were launched to address this need-the multi-center Beat AML initiative, led by the Oregon Health & Science University (OHSU) and the AML Individualized Systems Medicine program at the Institute for Molecular Medicine Finland (FIMM). Methods: We performed a comparative analysis of the two large-scale data sets in which patient samples were subjected to whole-exome sequencing, RNA-seq, and ex vivo functional drug sensitivity screens: OHSU (121 patients and 160 drugs) and FIMM (39 patients and 480 drugs). We predicted ex vivo drug response [quantified as area under the dose-response curve (AUC)] using gene expression signatures selected with standard regression and a novel Bayesian model designed to analyze multiple data sets simultaneously. We restricted analysis to the 95 drugs in common between the two data sets. Results: The ex vivo responses (AUCs) of most drugs were positively correlated (OHSU: median Pearson correlation r across all pairwise drug comparisons=0.27; FIMM: median r=0.33). Consistently, a samples's ex vivo response to an individual drug was often correlated with the patient's Average ex vivo Drug Sensitivity (ADS), i.e., the average response across the 95 drugs (OHSU: median r across 95 drugs=0.41; FIMM: median r=0.58). Patients with a complete response to standard induction therapy had a higher ADS than those that were refractory (p=0.01). Further, patients whose ADS was in the top quartile had improved overall survival relative to those having an ADS in the bottom quartile (p<0.05). Standard regression models (LASSO and Ridge) trained on ADS and gene expression in the OHSU data set had improved ex vivo response prediction performance as assessed in the independent FIMM validation data set relative to those trained on gene expression alone (LASSO: p=2.9x10-4; Ridge: p=4.4x10-3). Overall, ex vivo drug response was relatively well predicted (LASSO: mean r across 95 drugs=0.62; Ridge: mean r=0.62). The BCL-2 inhibitor venetoclax was the only drug whose response was negatively correlated with ADS in both data sets. We hypothesized that, whereas the predictive performance of many other drugs was likely dependent on ADS, the predictive performance of venetoclax (LASSO: r=0.53, p=0.01; Ridge: r=0.63, p=1.3x10-3) reflected specific gene expression biomarkers. To identify biomarkers associated with venetoclax sensitivity, we developed an integrative Bayesian machine learning method that jointly modeled both data sets, revealing several candidate biomarkers positively (BCL2 and FLT3) or negatively (CD14, MAFB, and LRP1) correlated with venetoclax response. We assessed these biomarkers in an independent data set that profiled ex vivo response to the BCL-2/BCL-XL inhibitor navitoclax in 29 AML patients (Lee et al.). All five biomarkers were validated in the Lee data set (Fig 1). Conclusions: The two independent ex vivo functional screens were highly concordant, demonstrating the reproducibility of the assays and the opportunity for their use in the clinic. Joint analysis of the two data sets robustly identified biomarkers of drug response for BCL-2 inhibitors. Two of these biomarkers, BCL2 and the previously-reported CD14, serve as positive controls credentialing our approach. CD14, MAFB, and LRP1 are involved in monocyte differentiation. The inverse correlation of their expression with venetoclax and navitoclax response is consistent with prior reports showing that monocytic cells are resistant to BCL-2 inhibition (Kuusanmäki et al.). These biomarker panels may enable better selection of patient populations likely to respond to BCL-2 inhibition than would any one biomarker in isolation. References: Kuusanmäki et al. (2017) Single-Cell Drug Profiling Reveals Maturation Stage-Dependent Drug Responses in AML, Blood 130:3821 Lee et al. (2018) A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia, Nat Commun 9:42 Disclosures Druker: Cepheid: Consultancy, Membership on an entity's Board of Directors or advisory committees; ALLCRON: Consultancy, Membership on an entity's Board of Directors or advisory committees; Fred Hutchinson Cancer Research Center: Research Funding; Celgene: Consultancy; Vivid Biosciences: Membership on an entity's Board of Directors or advisory committees; Aileron Therapeutics: Consultancy; Third Coast Therapeutics: Membership on an entity's Board of Directors or advisory committees; Oregon Health & Science University: Patents & Royalties; Patient True Talk: Consultancy; Millipore: Patents & Royalties; Monojul: Consultancy; Gilead Sciences: Consultancy, Membership on an entity's Board of Directors or advisory committees; Amgen: Membership on an entity's Board of Directors or advisory committees; Leukemia & Lymphoma Society: Membership on an entity's Board of Directors or advisory committees, Research Funding; GRAIL: Consultancy, Membership on an entity's Board of Directors or advisory committees; Beta Cat: Membership on an entity's Board of Directors or advisory committees; MolecularMD: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Henry Stewart Talks: Patents & Royalties; Bristol-Meyers Squibb: Research Funding; Blueprint Medicines: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; Aptose Therapeutics: Consultancy, Equity Ownership, Membership on an entity's Board of Directors or advisory committees; McGraw Hill: Patents & Royalties; ARIAD: Research Funding; Novartis Pharmaceuticals: Research Funding. Heckman:Orion Pharma: Research Funding; Novartis: Research Funding; Celgene: Research Funding. Porkka:Novartis: Honoraria, Research Funding; Celgene: Honoraria, Research Funding. Tyner:AstraZeneca: Research Funding; Incyte: Research Funding; Janssen: Research Funding; Leap Oncology: Equity Ownership; Seattle Genetics: Research Funding; Syros: Research Funding; Takeda: Research Funding; Gilead: Research Funding; Genentech: Research Funding; Aptose: Research Funding; Agios: Research Funding. Aittokallio:Novartis: Research Funding. Wennerberg:Novartis: Research Funding.

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Chavan, Mr Vikram. "Malware Classification using Machine Learning Algorithms and Tools." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 10, 2021): 69–73. http://dx.doi.org/10.22214/ijraset.2021.34353.

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The explosive growth of malware variants poses a major threat to information security. Malware is the one which frequently growing day by day and becomes major threats to the Internet Security. According to numerous increasing of worm malware in the networks nowadays, it became a serious danger that threatens our computers. Networks attackers did these attacks by designing the worms. A designed system model is needed to defy these threats, prevent it from multiplying and spreading through the network, and harm our computers. In this paper, we designed a classification on system model for this issue. The designed system detects the worm malware that depends on the information of the dataset that is taken from website, the system will receive the input package and then analyze it, the Naïve Bayesian classification technique will start to work and begin to classify the package, by using the data mining Naïve Bayesian classification technique, the system worked fast and gained great results in detecting the worm. By applying the Naïve Bayesian classification technique using its probability mathematical equations for both threat data and benign data, the technique will detect the malware and classify data whether it was threat or benign.

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Nixon, Matthew C., and Nikku Madhusudhan. "Assessment of supervised machine learning for atmospheric retrieval of exoplanets." Monthly Notices of the Royal Astronomical Society 496, no. 1 (June 16, 2020): 269–81. http://dx.doi.org/10.1093/mnras/staa1150.

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ABSTRACT Atmospheric retrieval of exoplanets from spectroscopic observations requires an extensive exploration of a highly degenerate and high-dimensional parameter space to accurately constrain atmospheric parameters. Retrieval methods commonly conduct Bayesian parameter estimation and statistical inference using sampling algorithms such as Markov chain Monte Carlo or Nested Sampling. Recently several attempts have been made to use machine learning algorithms either to complement or to replace fully Bayesian methods. While much progress has been made, these approaches are still at times unable to accurately reproduce results from contemporary Bayesian retrievals. The goal of this work is to investigate the efficacy of machine learning for atmospheric retrieval. As a case study, we use the Random Forest supervised machine learning algorithm which has been applied previously with some success for atmospheric retrieval of the hot Jupiter WASP-12b using its near-infrared transmission spectrum. We reproduce previous results using the same approach and the same semi-analytic models, and subsequently extend this method to develop a new algorithm that results in a closer match to a fully Bayesian retrieval. We combine this new method with a fully numerical atmospheric model and demonstrate excellent agreement with a Bayesian retrieval of the transmission spectrum of another hot Jupiter, HD 209458b. Despite this success, and achieving high computational efficiency, we still find that the machine learning approach is computationally prohibitive for high-dimensional parameter spaces that are routinely explored with Bayesian retrievals with modest computational resources. We discuss the trade-offs and potential avenues for the future.

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Lehto, M. R., and G. S. Sorock. "Machine Learning of Motor Vehicle Accident Categories from Narrative Data." Methods of Information in Medicine 35, no. 04/05 (September 1996): 309–16. http://dx.doi.org/10.1055/s-0038-1634680.

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Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

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Hwang, Ha-Eun, Yoon-Sang Cho, Seok-Cheol Hwang, and Seoung-Bum Kim. "Optimal Tire Design Using Machine Learning and Bayesian Optimization." Journal of the Korean Institute of Industrial Engineers 48, no. 4 (August 31, 2022): 433–40. http://dx.doi.org/10.7232/jkiie.2022.48.4.433.

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Baggio, Giacomo, Algo Carè, Anna Scampicchio, and Gianluigi Pillonetto. "Bayesian frequentist bounds for machine learning and system identification." Automatica 146 (December 2022): 110599. http://dx.doi.org/10.1016/j.automatica.2022.110599.

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Williams, Dominic P., Stanley E. Lazic, Alison J. Foster, Elizaveta Semenova, and Paul Morgan. "Predicting Drug-Induced Liver Injury with Bayesian Machine Learning." Chemical Research in Toxicology 33, no. 1 (September 19, 2019): 239–48. http://dx.doi.org/10.1021/acs.chemrestox.9b00264.

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Wang, Hui. "Finding patterns in subsurface using Bayesian machine learning approach." Underground Space 5, no. 1 (March 2020): 84–92. http://dx.doi.org/10.1016/j.undsp.2018.10.006.

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Wang, Jian, Ting Ran, Yadong Chen, and Tao Lu. "Bayesian machine learning to discover Bruton’s tyrosine kinase inhibitors." Chemical Biology & Drug Design 96, no. 4 (August 18, 2020): 1114–22. http://dx.doi.org/10.1111/cbdd.13656.

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Garcia-Bonete, Maria-Jose, and Gergely Katona. "Bayesian machine learning improves single-wavelength anomalous diffraction phasing." Acta Crystallographica Section A Foundations and Advances 75, no. 6 (October 7, 2019): 851–60. http://dx.doi.org/10.1107/s2053273319011446.

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Single-wavelength X-ray anomalous diffraction (SAD) is a frequently employed technique to solve the phase problem in X-ray crystallography. The precision and accuracy of recovered anomalous differences are crucial for determining the correct phases. Continuous rotation (CR) and inverse-beam geometry (IBG) anomalous data collection methods have been performed on tetragonal lysozyme and monoclinic survivin crystals and analysis carried out of how correlated the pairs of Friedel's reflections are after scaling. A multivariate Bayesian model for estimating anomalous differences was tested, which takes into account the correlation between pairs of intensity observations and incorporates the a priori knowledge about the positivity of intensity. The CR and IBG data collection methods resulted in positive correlation between I(+) and I(−) observations, indicating that the anomalous difference dominates between these observations, rather than different levels of radiation damage. An alternative pairing method based on near simultaneously observed Bijvoet's pairs displayed lower correlation and it was unsuccessful for recovering useful anomalous differences when using the multivariate Bayesian model. In contrast, multivariate Bayesian treatment of Friedel's pairs improved the initial phasing of the two tested crystal systems and the two data collection methods.

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Santucci, Raymond J., Christine E. Sanders, Hongyu Zhu, Kenneth D. Smith, and Robert G. Kelly. "Bayesian Network Machine Learning Approach to Atmospheric Corrosion Modelling." ECS Meeting Abstracts MA2022-02, no. 10 (October 9, 2022): 693. http://dx.doi.org/10.1149/ma2022-0210693mtgabs.

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The performance degradation of materials exposed to corrosive atmospheric environments is a serious problem. Corrosion maintenance strategies and cycles are informed largely by historical trends in corrosivity dependent on the location of interest. Assessments are often made with sparse experimental data and are generalized to certain materials and baseline conditions observed in the past. The development of a predictive model which can better inform maintenance strategies and cycles offers opportunities for time savings and cost avoidance. Such a model would need to predict corrosion damage accumulation as a function of material of interest, location of interest, and atmospheric conditions during the period of exposure of interest. Machine learning has recently gained attention as a way to model complex systems and processes. Atmospheric corrosion is certainly a complex process as there are many parameters that influence it which are uncontrolled and vary in difficult to predict ways. A machine learning algorithm would require data to learn from which might include environmental conditions such as temperature, relative humidity, UV light exposure, time of wetness, salt concentration, applied loads, and electrochemical data for substrates, inhibitors, and coatings. The ability of machine learning to determine models and relationships from large data sets is well demonstrated; however, a Bayesian model approach can be more suitable when data is limited, expensive or time consuming to obtain, and expert knowledge can be leveraged to construct the relationships. While certain meteorological variables are well categorized and easy to retrieve other variables like corrosion damage or salt deposition are sparsely reported if measured at all. For this reason, a Bayesian Network Model has been developed to better predict the corrosion damage accumulation of C1010 steel and three different aerospace coating systems applied over AA2024-T351. The network map was constructed from parameters (nodes) which are known to influence corrosion. Mathematical relationships were used to develop relationships between nodes or to calculate new nodes as available. Finite element analysis was used to refine the network map and provide supplemental inputs for features that are difficult to measure experimentally. The model was initially trained from historic corrosion data gathered from collaborators across the DoD. Contemporary experimental data was also gathered in a 19-site survey of field exposure sites across the world. This dataset provided further inputs for training and also data for validation and testing. The methodology behind model construction will be highlighted and model predictions will be compared against experimental results. To date, there has been good correlation between model predictions and experimental results for C1010 steel mass loss, which provides a basis to further extend the model to aerospace coating systems. Figure 1

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Bessa, Miguel A., Piotr Glowacki, and Michael Houlder. "Bayesian Machine Learning in Metamaterial Design: Fragile Becomes Supercompressible." Advanced Materials 31, no. 48 (October 14, 2019): 1904845. http://dx.doi.org/10.1002/adma.201904845.

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Chen, Hongyu, Xinyi Li, Zongbao Feng, Lei Wang, Yawei Qin, Miroslaw J. Skibniewski, Zhen-Song Chen, and Yang Liu. "Shield attitude prediction based on Bayesian-LGBM machine learning." Information Sciences 632 (June 2023): 105–29. http://dx.doi.org/10.1016/j.ins.2023.03.004.

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Chaturvedi, Iti, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino, and Erik Cambria. "Bayesian network based extreme learning machine for subjectivity detection." Journal of the Franklin Institute 355, no. 4 (March 2018): 1780–97. http://dx.doi.org/10.1016/j.jfranklin.2017.06.007.

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