An enhanced Bayesian approach for damage identification utilizing prior knowledge from refined elemental modal strain energy ratios by Li Chen, Hui Chen, Lu-ling Liu

“…The proposed method better utilizes the advantages of prior information in the Bayesian method, making the identified damage more accurate. …”
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Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold by Fang Liu, Lanlan Zheng, Mingyang Li, Jinjun Tang

“…Traffic accident data set collected in Los Angeles city from February 2016 to June 2020 is used to validate prediction performance in this study. Bayesian method is applied to optimize the hyperparameters in the RF, while three evaluation indicators, including the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), are used to estimate the prediction effect. …”
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Water Quality Evaluation and Influencing Factor Analysis of Nanwan Reservoir with a Bayesian Model Based on AHM-CRITIC Combination Weighting by SUN Geyao, DUAN Jingjing, ZHAO Jing

“…In a traditional Bayesian model,equal weights are assigned to different evaluation factors,and the difference in their contributions to water quality is thereby ignored.To address this problem,this paper introduces the attribute hierarchy model-criteria importance through intercriteria correlation (AHM-CRITIC) combination weighting on the basis of a traditional Bayesian model and constructs a Bayesian model for water quality evaluation based on AHM-CRITIC combination weighting.The water quality of Nanwan Lake and the water quality grades of the main tributaries inflowing into Nanwan Reservoir are quantitatively analyzed with the model by using measured monthly water quality data of reservoir sections and main inflow tributaries from 2012 to 2020,and the results are compared with those of other methods.The following findings are obtained.① From 2012 to 2015,Nanwan Lake basically maintained the water quality at the Class III standard.However,from 2016 to 2020,the water quality did not meet the Class III standard occasionally,with water of Class Ⅳ even appearing.The water quality of the Dongjiahe section was poor,while the water quality of the Xiaoshihe,Tanjiahe,Wudaohe,and Xijiahe sections was good.② The results of the Bayesian model with combination weighting for water quality evaluation are more consistent with those of the single factor index method and the improved Nemerow index method.Overcoming the shortcomings of the subjectively weighted Bayesian method,the proposed model can reflect the water quality of the reservoir more realistically and provide a technical basis for similar water quality evaluation.…”
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Bayesian Estimation of Resistance Factor for Bored Piles Based on Load Test Database by Zhijun Xu, Ranran Zhang, Liang Fan, Xing Han, Fang Yuan, Mingfang Du

“…This paper employs the Bayesian method and mathematical statistics theory to put forward an estimation method for updating in situ data. …”
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Inferential Statistics from Black Hispanic Breast Cancer Survival Data by Hafiz M. R. Khan, Anshul Saxena, Elizabeth Ross, Venkataraghavan Ramamoorthy, Diana Sheehan

“…We used three common model building criteria which include Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), and Deviance Information Criteria (DIC) to measure the goodness of fit tests and it was found that Black Hispanic female patients survival data better fit the exponentiated exponential probability model. A novel Bayesian method was used to derive the posterior density function for the model parameters as well as to derive the predictive inference for future response. …”
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Proton response and neutron spectrum unfolding by solution-grown trans-stilbene scintillator by Nguyen Duy Quang, H. J. Kim, Sunghwan Kim, Sinchul Kang, Phan Quoc Vuong, Phan Bao Quoc Hieu, Nguyen Kien Cuong, Y. S. Yoon, Uk-Won Nam

“…The unfolding was performed using the iterative Bayesian method without any constraints, smoothness, or assumption of prior knowledge of original spectra. …”
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Prioritization of causal genes from genome-wide association studies by Bayesian data integration across loci. by Zeinab Mousavi, Marios Arvanitis, ThuyVy Duong, Jennifer A Brody, Alexis Battle, Nona Sotoodehnia, Ali Shojaie, Dan E Arking, Joel S Bader

“…<h4>Results</h4>We describe a new Bayesian method, termed SigNet for significance networks, that combines information both within and across loci to identify the most likely causal gene at each locus. …”
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Robust parameter design for constrained randomization lifetime improvement experiments by Shanshan Lv, Yichen Zhao, Sen Li, Guodong Wang, Xueqing Wang

“…To consider random effects, we develop a Bayesian method-based Weibull non-linear mixed model for the lifetime response. …”
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Confidence intervals for the COVID-19 neutralizing antibody retention rate in the Korean population by Catherine Apio, Md. Kamruzzaman, Taesung Park

“…In this article, we found that the Wald method gives the narrowest interval among all Asymptotic methods whereas mid p-value gives the narrowest among all Exact methods and Jeffrey’s method gives the narrowest from Bayesian method. The most conservative 95% confidence interval estimation shows that as of 00:00 on September 15, 2020, at least 32,602 people were infected but not confirmed in Korea.…”
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Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans by Lorenzo Venturini, Samuel Budd, Alfonso Farruggia, Robert Wright, Jacqueline Matthew, Thomas G. Day, Bernhard Kainz, Reza Razavi, Jo V. Hajnal

“…We then measure fetal biometrics in every frame where appropriate anatomy is visible. We use a Bayesian method to estimate the true value of each biometric from a large number of measurements and probabilistically reject outliers. …”
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Genetic parameters for milk production traits of Simmental cows with random regression test-day model by A. Otwinowska-Mindur, E. Ptak, W. Jagusiak, A. Zarnecki

“…The BLUPf90 package and a Bayesian method via Gibbs sampling were used to estimate (co)variance components. …”
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Systematic review and bayesian network meta-analysis: comparative efficacy and safety of six commonly used biologic therapies for moderate-to-severe Crohn’s disease by Haohang Su, Haohang Su, Shengwei Xiao, Zhiqing Liang, Zhiqing Liang, Tianrong Xun, Jinfang Zhang, Jinfang Zhang, Xixiao Yang, Xixiao Yang

“…BackgroundIn contrast to previous network meta-analysis using classical frequentist methods, we evaluated the efficacy and safety of six frequently-used biologics through a Bayesian method.MethodsWeb of Science, Scopus, CENTRAL, ClinicalTrials.gov and ICTRP were searched to collect randomized controlled trials (RCTs) in adults with moderate-to-severe Crohn’s disease, comparing Infliximab, Adalimumab, Certolizumab pegol, Ustekinumab, Risankizumab, or Vedolizumab, relative to placebo or an active comparator for induction of clinical response (two different definitions) and maintenance of clinical remission. …”
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Application of various canopy resistance calculation methods in vineyard evapotranspiration simulation at daily scale in Northwest China by Jingying Zhang, Dianyu Chen, Xiaotao Hu, Lucheng Qiu, Shujian Wang, Xuanrong Zhu, La Zhuo, Dongyan Zhang, Linzhe Wu, Linlin Zhang

“…The model parameter optimization was based on the Bayesian method. The accuracy evaluation indicators included relative error (MRE, %), coefficient of determination (R2) and Wilmott consistency coefficient (D). …”
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Some Applications of Bayes' Rule in Probability Theory to Electrocatalytic Reaction Engineering by Thomas Z. Fahidy

“…Bayesian methods stem from the principle of linking prior probability and conditional probability (likelihood) to posterior probability via Bayes' rule. …”
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A Bayesian tool for optimizing sample size in phase I dose-finding trials for head and neck cancer by Sivakamavalli Jeyachandran

“…This adaptability allows modifications based on interim results, enhancing patient safety without compromising trial integrity. Bayesian methods improve dose estimation precision, reduce required sample sizes, and facilitate informed decision-making throughout the trial. …”
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Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies by Bing Liu, Shishi Feng, Xuan Guo, Jing Zhang

“…We also provide a summary of the Bayesian methods’ applications toward these viruses’ studies, where several important and useful results have been discovered. …”
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Recent advances in Bayesian inference of isolation-with-migration models by Yujin Chung

“…Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. …”
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Nonparametric Bayesian dictionary learning algorithm based on structural similarity by Daoguang DONG, Guosheng RUI, Wenbiao TIAN, Jian KANG, Ge LIU

“…Though nonparametric Bayesian methods possesses significant superiority with respect to traditional comprehensive dictionary learning methods,there is room for improvement of this method as it needs more consideration over the structural similarity and variability of images.To solve this problem,a nonparametric Bayesian dictionary learning algorithm based on structural similarity was proposed.The algorithm improved the structural representing ability of dictionaries by clustering images according to their non-local structural similarity and introducing block structure into sparse representing of images.Denoising and compressed sensing experiments showed that the proposed algorithm performs better than several current popular unsupervised dictionary learning algorithms.…”
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Improving Probabilistic Error Cancellation in the Presence of Nonstationary Noise by Samudra Dasgupta, Travis S. Humble

“…In this article, we investigate the stability of probabilistic error cancellation (PEC) outcomes in the presence of nonstationary noise, which is an obstacle to achieving accurate observable estimates. Leveraging Bayesian methods, we design a strategy to enhance PEC stability and accuracy. …”
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Chain Graph Models to Elicit the Structure of a Bayesian Network by Federico M. Stefanini

“…Building the structure of large networks is still a challenging task, but Bayesian methods are particularly suited to exploit experts’ degree of belief in a quantitative way while learning the network structure from data. …”
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