Evaluating Bicycling Environments with Trajectory Data on Shared Bikes: A Case Study of Beijing by Ying Hu, Chunfu Shao, Shuling Wang, Hairui Sun, Pengfei Sun, Zhongfu Chu

“…First, according to the characteristics of the data and the riding environment, a boxplot method and Bayesian probabilistic network model are used to exclude abnormal data and to match trajectories to road sections. …”
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Interpretable Combinatorial Machine Learning-Based Shale Fracability Evaluation Methods by Di Wang, Dingyu Jiao, Zihang Zhang, Runze Zhou, Weize Guo, Huai Su

“…In this paper, an interpretable combinatorial machine learning shale fracability evaluation method is proposed, which combines XGBoost and Bayesian optimization techniques to mine the non-linear relationship between the influencing factors and fracability, and to achieve more accurate fracability evaluations with a lower error rate (maximum MAPE not more than 20%). …”
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Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties by Xueteng Wang, Jiandong Wang, Mengyao Wei, Yang Yue

“…Structural errors in these models under varying operating conditions result in noticeable model uncertainties. Second, Bayesian estimation theory and the Markov Chain Monte Carlo approach are employed to analyze the differences between historical data and model predictions under varying operating conditions, thereby quantifying modeling uncertainties. …”
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Integrated Gaussian Processes for Tracking by Fred Lydeard, Bashar I. Ahmad, Simon Godsill

“…We further show how to implement the model with fixed time complexity in a standard sequential Bayesian updating framework using Kalman filter-based computations, employing a sliding window Markovian approximation. …”
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A novel quantile regression for fractiles based on unit logistic exponential distribution by Hanan Haj Ahmad, Kariema A. Elnagar

“…The statistical properties of the ULE distribution were studied, and parameter estimation through maximum likelihood estimation, Bayesian methods, maximum product spacings, and least squares estimates were conducted. …”
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Anodal tDCS over Primary Motor Cortex Provides No Advantage to Learning Motor Sequences via Observation by Dace Apšvalka, Richard Ramsey, Emily S. Cross

“…The results demonstrated that active stimulation provided no advantage to skill learning over sham stimulation. Further, Bayesian analyses revealed evidence in favour of the null hypothesis across our dependent measures. …”
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Global, regional, and national burden of NAFLD in youths and young adults aged 15–39 years, 1990–2021, its attributable risk factors, and projections to 2035: a systematic analysis... by Jiong Wang, Jiqing Du, Minxiu Wang, Mengyun Jin, Zhihua Tang, Yuqin Mao

“…We evaluated the global temporal trend of NAFLD from 1990 to 2021 with estimated annual percentage change (EAPC) and age-standardized rate (ASR). The Bayesian age-period-cohort (BAPC) model was used to predict future trends of the NAFLD burden to 2035. …”
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Viscoelastic Fluid Factor Inversion and Application in Luojia Oilfield Based on Broadband Impedance by Ruyi Zhang, Huazhong Wang

“…Using the frequency-dependent viscoelastic impedance equation and Bayesian inversion framework, the objective function of frequency-dependent elastic impedance inversion can be established to realize the frequency-dependent impedance inversion at different angles. …”
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The impact of musical expertise and directional isotropy on the proportions and magnitudes of pitch-shift responses in glissandos by Li-Hsin Ning

“…Response proportions for opposing and following responses were estimated through Bayesian Poisson regression modeling, whereas response magnitudes were scrutinized using generalized additive mixed effects modeling.ResultsOur results revealed that opposing and following responses were less pronounced among musicians compared to non-musicians. …”
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A Hierarchical Probabilistic Framework for Recognizing Learners’ Interaction Experience Trends and Emotions by Imène Jraidi, Maher Chaouachi, Claude Frasson

“…We propose a hierarchical probabilistic framework using a dynamic Bayesian network to model this relationship and to simultaneously recognize the probability of experiencing each trend as well as the emotional responses occurring subsequently. …”
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Assessing Africa's child survival gains and prospects for attaining SDG target on child mortality. by Sunday A Adedini, Seun Stephen Anjorin, Jacob Wale Mobolaji, Elvis Anyaehiechukwu Okolie, Sanni Yaya

“…Results of the predictions using supervised machine learning on the Bayesian network reveal that the probability of achieving the SDG target 3.2 (i.e., having U5MR of 25 deaths per 1000 live births or less) increases (from 21.6% to 100%) when the contraceptive prevalence increases from 49.8% to 78.5%; and the use of skilled birth attendants increases from 44.8% to 86.3%; and percentage of secondary school completion of female increases from 42.5 to 74.0%. …”
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燕科和雨燕科部分鸟类系统发育比较分析 by 刘方庆 胡梦英 文陇英

“…本文通过选择物燕科8个物种以及雨燕科5个物种,以线粒体基因Cyt <i>b</i>作为分子标记,通过分子生物学手段对燕科和雨燕科部分鸟类进行重建系统发育树,采用贝叶斯推论法(Bayesian Inference)和最大似然法(ML,Maximum Likelihood)以验证分子生物学分类结果是否与宏观形态学分类结果相一致。…”
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Effect of the Ensemble System Size on the Precipitation Forecast Accuracy by Atefeh Mohamadi, Majid Azadi

“…There are several methods for post-processing of ensemble forecasting, including Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) that they are more popular because of higher efficiency and accuracy. …”
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Modelling Customs Revenue in Ghana Using Novel Time Series Methods by Diana Ayorkor Agbenyega, John Andoh, Samuel Iddi, Louis Asiedu

“…In this study, we modelled monthly revenue data obtained from the Ghana Revenue Authority-Customs Division (GRA-CD) for the period January 2010 to December 2019 using two traditional time series models, ARIMA model and ARIMA Error Regression Model (ARIMAX), and two machine learning time series models, Bayesian Structural Time Series (BSTS) model and a Neural Network Autoregression model. …”
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Structural Analysis and Total Coal Demand Forecast in China by Qing Zhu, Zhongyu Zhang, Rongyao Li, Kin Keung Lai, Shouyang Wang, Jian Chai

“…After extracting the core factors, the Bayesian vector autoregressive forecast model is constructed, with variables that include coal consumption, the gross value of industrial output, and the downstream industry output (cement, crude steel, and thermal power). …”
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Enhancing insights in sexually transmitted infection mapping: Syphilis in Forsyth County, North Carolina, a case study. by Lani Fox, William C Miller, Dionne Gesink, Irene Doherty, Marc Serre

“…Our methodology extends current models with: 1) donut geomasking to enhance resolution while protecting patient privacy; 2) a moving window uniform grid to control the modifiable areal unit problem, edge effect and remove kriging islands; and 3) mitigating the "small number problem" with Uniform Model Bayesian Maximum Entropy (UMBME). Data is 2008-2011 early syphilis cases reported to the NC Department of Health and Human Services for Forsyth County. …”
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Regulation of meristem and hormone function revealed through analysis of directly-regulated SHOOT MERISTEMLESS target genes by Tamara Lechon, Nicholas A. Kent, James A. H. Murray, Simon Scofield

“…Finally, we use a subset of these target genes to perform Bayesian network analysis to infer gene regulatory associations and to construct a refined GRN for STM-mediated control of meristem function.…”
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DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection by Shaofu Lin, Yang Yang, Xiliang Liu, Li Tian

“…Secondly, a hybrid loss function has been developed with weights optimized employing the Bayesian Optimization algorithm to provide a strategic method for parameter tuning in similar research. …”
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Real-world pharmacovigilance analysis unveils the toxicity profile of amivantamab targeting EGFR exon 20 insertion mutations in non-small cell lung cancer by Jing Zhang, Wenjie Li

“…A comprehensive disproportionality analysis was performed, employing the reporting odds ratio (ROR), proportional reporting ratio (PRR), Empirical Bayes Geometric Mean (EBGM), and the Bayesian confidence propagation neural network to calculate information components (ICs), to identify statistically significant adverse events. …”
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An intensification of surface Earth’s energy imbalance since the late 20th century by Xuqian Li, Qingxiang Li, Martin Wild, Phil Jones

“…We have employed the Bayesian Model Averaging (BMA) method, integrating it with recent developments in surface solar radiation observational data, to refine the ensemble of CMIP6 model outputs. …”
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