Novel cooperative global spectrum sensing algorithm based on variational Bayesian inference by Ming WU, Tie-cheng SONG, Jing HU, Lian-feng SHEN

“…Then, an estimator of mod-el coefficient vector was designed by utilizing the th of variational Bayesian inference (VBI). Simulation results show that the proposed approximate model has good accuracy, and the corresponding estimation algorithm of model coefficient vector has good convergence and stability. …”
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Bayesian Robust Tensor Decomposition Based on MCMC Algorithm for Traffic Data Completion by Longsheng Huang, Yu Zhu, Hanzeng Shao, Lei Tang, Yun Zhu, Gaohang Yu

“…The low CP rank tensor is modeled by linear interrelationships among multiple latent factors, and the sparsity of the columns on the latent factors is achieved through a hierarchical prior approach, while the sparse tensor is modeled by a hierarchical view of the Student-t distribution. …”
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Rainfall data modeling using improved adaptive type-II progressively censored Weibull-exponential samples by Refah Alotaibi, Mazen Nassar, Ahmed Elshahhat

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A New Prediction Method of Wind Power Based on L2 Norm Cloud to Erase the Wind Uncertainty by Fangyu Wang, Liping Zhu, Wenying Liu, Jie Qin

“…Secondly, in order to avoid the error increase of the combined cloud model caused by the sample peak disturbance, L2 norm theory is introduced to update the peak point to enhance the robustness of the model. …”
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Implicit versus explicit Bayesian priors for epistemic uncertainty estimation in clinical decision support. by Malte Blattmann, Adrian Lindenmeyer, Stefan Franke, Thomas Neumuth, Daniel Schneider

“…However, their reliability degrades on out-of-distribution inputs, and traditional point-estimate predictors can give overconfident outputs even in regions where the model has little evidence. …”
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A Caputo–Fabrizio fractional-order model with MCMC estimation for rabies transmission dynamics in a multi-host population by Jufren Zakayo Ndendya, Joshua A. Mwasunda, Stephen Edward, Nyimvua Shaban

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Uncertainty-aware diabetic retinopathy detection using deep learning enhanced by Bayesian approaches by Mohsin Akram, Muhammad Adnan, Syed Farooq Ali, Jameel Ahmad, Amr Yousef, Tagrid Abdullah N. Alshalali, Zaffar Ahmed Shaikh

“…Traditional deep learning models rely on single-point predictions, limiting their ability to provide uncertainty measures essential for robust clinical decision-making. …”
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Crypto Asset Markets vs. Financial Markets: Event Identification, Latest Insights and Analyses by Eleni Koutrouli, Polychronis Manousopoulos, John Theal, Laura Tresso

“…For the latter analyses, we adopt a Bayesian model averaging approach to identify change points in the Bitcoin and Ethereum daily price time series. …”
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An Adaptive Robust Event-Triggered Variational Bayesian Filtering Method with Heavy-Tailed Noise by Di Deng, Peng Yi, Junlin Xiong

“…The one-step state prediction probability density function and the measurement likelihood function are modeled as Student’s t-distributions. By choosing inverse Wishart priors, the system state, the prediction error covariance, and the measurement noise covariance are jointly estimated based on the variational Bayesian inference and the fixed-point iteration. …”
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A novel Bayesian generative approach for estimating tumor dynamics from published studies by Arya Pourzanjani, Saurabh Modi, Jamie Connarn, Xinwen Zhang, Vijay Upreti, Chih‐Wei Lin, Khamir Mehta

“…TGI models present several advantages over traditional exposure–response models that are based explicitly on clinical end points and have become a popular tool in the pharmacometrics community. …”
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Anatomical Plausibility in Deformable Image Registration Using Bayesian Optimization for Brain MRI Analysis by Mauricio Castaño-Aguirre, Hernán Felipe García, David Cárdenas-Peña, Gloria Liliana Porras-Hurtado, Álvaro Ángel Orozco-Gutiérrez

“…These datasets include brain scans taken at multiple time points, enabling the modeling of structural changes over time. …”
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A data driven change-point epidemic model for assessing the impact of large gathering and subsequent movement control order on COVID-19 spread in Malaysia. by Sarat C Dass, Wai M Kwok, Gavin J Gibson, Balvinder S Gill, Bala M Sundram, Sarbhan Singh

“…Further, a change-point is incorporated to model disease dynamics before and after intervention which is inferred based on data. …”
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A Comprehensive Reliability Assessment Method for Existing Railway Bridges Based on Bayesian Theory by Zhonglong Li, Xiaowei Chen, Haonan Bing, Yangjun Zhao, Zhifeng Ye

“…To construct a comprehensive assessment method for the safety and reliability of existing railway bridges, firstly, the risk factors of railway bridge structures are analyzed and the evaluation criteria are determined; secondly, based on the accident tree theory, a multilevel Bayesian network model with key points is established, and the ability of the Bayesian network bidirectional reasoning and sensitivity analysis is used to evaluate the structural safety; finally, the result was applied to the marina northern Songhua River extra-large bridge to verify the applicability of the comprehensive evaluation of the reliability of an existing railway bridge. …”
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Causal AI Recommendation System for Digital Mental Health: Bayesian Decision-Theoretic Analysis by Mathew Varidel, Victor An, Ian B Hickie, Sally Cripps, Roman Marchant, Jan Scott, Jacob J Crouse, Adam Poulsen, Bridianne O'Dea, Sarah McKenna, Frank Iorfino

“…The causal processes are assumed to follow a structural causal model, where the posterior distribution of structural causal models is estimated using a Markov chain Monte Carlo method. …”
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Hydrological Simulation and Error Correction in Source Area of Yellow River Based on VIC-BJP Model by ZHU Changchang, HUANG Huaping

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Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification by Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing

“…By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM‐H indices. …”
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Reliability assessment of two production lines using joint progressively type-II censored XLindley samples by I. Elbatal, Ahmed Elshahhat, H. E. Semary, Mazen Nassar

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A template Bayesian network for combining forensic evidence on an item with an uncertain relation to the disputed activities by M. Vink, J.A. de Koeijer, M.J. Sjerps

“…Throughout the paper, we use a fictive case example that captures the essence of cases for which the template model can be used. The template BN provides a flexible starting point that can be adapted to specific case situations and supports structured probabilistic reasoning by a forensic scientist.…”
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Socio-economic factors boosting the effectiveness of marine protected areas: A Bayesian network analysis by Antonio Di Cintio, Jose Antonio Fernandes-Salvador, Riikka Puntila-Dodd, Igor Granado, Federico Niccolini, Fabio Bulleri

“…Our study introduces a model based on the Bayesian network that allows testing how different socioeconomic factors (e.g., stakeholder involvement, increased communication and enforcement) can impact the effectiveness of MPAs. …”
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Bayesian Analysis of Doubly Inflated Poisson Regression for Correlated Count Data: Application to DMFT Data by Bahare Gholami Chaboki, Alireza Akbarzadeh Baghban, Taban Baghfalaki, Mohammad Hossein Khoshnevisan, Maryam Heydarpour Meymeh

“…Outcome variables in clinical studies sometimes include count data with inflation in two points (usually zero and k (k>0)). Doubly inflated models can be adopted for modeling these types of data. …”
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