An Effective 3D Instance Map Reconstruction Method Based on RGBD Images for Indoor Scene by Heng Wu, Yanjie Liu, Chao Wang, Yanlong Wei

“…We integrate these point cloud segments into a global voxel map, updating each voxel’s class using color, distance constraints, and Bayesian methods to create an object-level instance map. …”
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Bayesian estimation of orientation and direction tuning captures parameter uncertainty by Zongting Wu, Stephen D. Van Hooser

“…Unlike traditional methods such as least squares, Bayesian estimation adeptly handles the probabilistic nature of neuronal responses, offering robust analysis even with limited data and weak selectivity. …”
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Calibration and Uncertainty Analysis of Freundlich and Langmuir Isotherms Using the Markov Chain Monte Carlo (MCMC) Approach by Haniyeh Sharifi Moghadam, Saeed Alimohammadi

“…To analyze parameter uncertainty, a Bayesian approach employing the Markov Chain Monte Carlo method was adopted, utilizing the Metropolis-Hastings and Gibbs algorithms, and the results were compared. …”
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Inference of Impulse Responses via Bayesian Graphical Structural VAR Models by Daniel Felix Ahelegbey

“…However, traditional IRF estimation methods often have limitations with assumptions on variable ordering and restrictive identification constraints. …”
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Bayesian optimization of underground railway tunnels using a surrogate model by Hassan Liravi, Hoang-Giang Bui, Sakdirat Kaewunruen, Aires Colaço, Jelena Ninić

“…The synthetic dataset is constructed using an accurate numerical model that integrates the two-and-a-half-dimensional singular boundary method for modeling wave propagation in the soil with the finite element method for structural modeling. …”
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Comprehensive Parameter Determination of Exoplanets through Asteroseismic Host Star Constraints by Wen-Xu Lin, Sheng-Bang Qian, Li-Ying Zhu, Wen-Ping Liao, Fu-Xing Li, Xiang-Dong Shi, Lin-Jia Li, Er-Gang Zhao

“…These stellar parameters are incorporated as priors in a Bayesian framework to refine planetary properties such as the mass, radius, and orbital parameters. …”
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An Unsupervised Remote Sensing Image Change Detection Method Based on RVMamba and Posterior Probability Space Change Vector by Jiaxin Song, Shuwen Yang, Yikun Li, Xiaojun Li

“…The experimental results on seven change detection datasets confirmed that the proposed method outperforms five state-of-the-art competitive CD methods.…”
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A 20‐year mapping of Bayesian belief networks in software project management by Abraham L. R. deSousa, Cleidson R. B. deSouza, Rodrigo Q. Reis

“…Abstract In recent years, more software engineering researchers have focussed on methods, techniques, tools, and processes to support software project management (SPM) addressing quality, cost, and time constraints. …”
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Reliability Analysis of Vehicle Braking System Based on Hyperellipsoidal Dynamic Bayesian Network by Yingjie Tian, Jing Wen, Shubin Zheng

“…This solves the conservatism problem of interval Bayesian networks. This suggests that the method improves the accuracy of brake system reliability analysis.…”
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Constraint-aware wind power forecasting with an optimized hybrid machine learning model by Md. Omer Faruque, Md. Alamgir Hossain, S.M. Mahfuz Alam, Muhammad Khalid

“…The proposed GAMO outperformed these methods in computational efficiency, reducing computation time by 74.20% compared to BOP and 90.91% compared to PSO. …”
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Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study. by Oliver Ratmann, Gé Donker, Adam Meijer, Christophe Fraser, Katia Koelle

“…We propose using a simulation-based, Bayesian method known as Approximate Bayesian Computation (ABC) to fit and assess phylodynamic models that simulate pathogen evolution and ecology against summaries of these data. …”
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Embracing uncertainty in cerebrospinal fluid dynamics: A Bayesian approach to analysing infusion studies by Jeremi Chabros, Michal M. Placek, Ka Hing Chu, Erta Beqiri, Peter J. Hutchinson, Zofia Czosnyka, Marek Czosnyka, Alexis Joannides, Peter Smielewski

“…Research question: This study aims to circumvent these constraints by introducing an innovative analysis method grounded in Bayesian inference. …”
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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

“…Our method uses probabilistic modeling to optimize non-rigid transformations, providing smooth and interpretable deformations that align with anatomical constraints. …”
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Adaptive bayesian sparse polynomial chaos expansion for voltage balance of an isolated microgrid at peak load by Sunil Kumar, Rashmi Agarwal, Harivardhagini Subhadra

“…One of the significant challenges faced is maintaining voltage balance during peak load periods, which is complicated by uncertainties in renewable energy availability, demand response, and system constraints. This paper introduces an Adaptive Bayesian Sparse Polynomial Chaos Expansion (BSPCE) framework designed to tackle these challenges effectively. …”
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Risk Assessment of Hydrogen-Powered Aircraft: An Integrated HAZOP and Fuzzy Dynamic Bayesian Network Framework by Xiangjun Dang, Yongxuan Shao, Haoming Liu, Zhe Yang, Mingwen Zhong, Huimin Zhao, Wu Deng

“…In this context, this study proposes a comprehensive hazard and operability analysis-fuzzy dynamic Bayesian network (HAZOP-FDBN) framework, which quantifies risk without relying on historical data. …”
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Seismogenic stress field estimation in the Calabrian Arc region (south Italy) from a Bayesian approach by C. Totaro, B. Orecchio, D. Presti, S. Scolaro, G. Neri

“…The new database has then been used for computation of posterior density distributions of stress tensor components by a Bayesian method never applied in south Italy before the present study. …”
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Bayesian state estimation for partially observable distribution networks via power flow-informed neural networks by Dong Liang, Guirong Li, Xiaoyu Liu, Lin Zeng, Hsiao-Dong Chiang, Shouxiang Wang

“…The conditional expectation is then viewed as a nonparametric regression that can be parameterized by a PFINN where a physics loss penalty is introduced into the loss function to constrain the neural network outputs to be consistent with power network operating constraints. Test results on balanced and unbalanced distribution networks using field data show that the proposed method can achieve better estimation accuracy than the Bayesian SE method without power flow informing. …”
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Bayesian tip-dated timeline for diversification and major biogeographic events in Muroidea (Rodentia), the largest mammalian radiation by Raquel López-Antoñanzas, Tiago R. Simões, Fabien L. Condamine, Moritz Dirnberger, Pablo Peláez-Campomanes

“…The development of phylogenetic methods to study evolutionary processes through time has revolutionized the field of evolutionary biology and led to an unprecedented expansion of our knowledge of the tree of life. …”
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An Adaptive Combined Filtering Algorithm for Non-Holonomic Constraints with Time-Varying and Thick-Tailed Measurement Noise by Zijian Wang, Jianghua Liu, Jinguang Jiang, Jiaji Wu, Qinghai Wang, Jingnan Liu

“…On the basis of this method, a Gaussian Student’s T distribution variational Bayesian filtering algorithm (Ga-St VBAKF) based on NHC pseudo-velocity measurement noise modeling is proposed through modeling and analysis of pseudo-velocity measurement noise. …”
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Novel machine learning driven design strategy for high strength Zn Alloys optimization with multiple constraints by Chenfeng Pan, Wenwen Lin, Jianxing Zhou, Wei Jian, Ka Chun Chan, Yuk Lun Chan, Lu Ren

“…Interpretability analysis of the models was performed using the SHAP method with particle swarm optimization (PSO). Furthermore, a ML-based Zn alloy composition design system (ZACDS) was proposed by integrating the Bayesian optimization algorithm. …”
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