Optimizing a Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences by Madhur Mangalam, Taylor J. Wilson, Joel H. Sommerfeld, Aaron D. Likens

“…The Bayesian Hurst–Kolmogorov (HK) method estimates the Hurst exponent of a time series more accurately than the age-old Detrended Fluctuation Analysis (DFA), especially when the time series is short. …”
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Constraints on power law and exponential models in f(Q) gravity by Dalale Mhamdi, Farida Bargach, Safae Dahmani, Amine Bouali, Taoufik Ouali

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Constraint-Based Bayesian Network Structure Learning using Uncertain Experts’ Knowledge by Christophe Gonzales, Axel Journe, Ahmed Mabrouk

“…In this paper, we fill this gap by introducing the mathematical foundations for new independence tests including this kind of information. We provide a new constraint-based algorithm relying on these tests as well as experiments that highlight the robustness of our method and its benefits compared to other constraint-based learning algorithms.…”
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A Variational Bayesian Truncated Adaptive Filter for Uncertain Systems with Inequality Constraints by Tianli Ma, Rong Zhang, Song Gao, Hong Li, Yang Zhang

“…In this paper, a variational Bayesian (VB) truncated adaptive filter for uncertain systems with inequality constraints is proposed. …”
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Optimizing Rotary Cement Kiln modelling: A comparative analysis of metaheuristics in a real-world application by Miguel Ángel Castán-Lascorz, Antonio Alcaide-Moreno, Jorge Arroyo

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Stochastic Fractal Search for Bayesian Network Structure Learning Under Soft/Hard Constraints by Yinglong Dang, Xiaoguang Gao, Zidong Wang

“…Moreover, a new feature selection (FS) method is proposed to mine fragmented knowledge. This fragmented prior knowledge serves as a soft constraint, and the acquired expert knowledge serves as a hard constraint. …”
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A Novel Hyper-Heuristic Algorithm for Bayesian Network Structure Learning Based on Feature Selection by Yinglong Dang, Xiaoguang Gao, Zidong Wang

“…Bayesian networks (BNs) are effective and universal tools for addressing uncertain knowledge. …”
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Constraints on Fast Radio Burst Population from the First CHIME/FRB Catalog with the Hierarchical Bayesian Inference by Huan Zhou, Zhengxiang Li, Zong-Hong Zhu

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Effects of Neural Assembles in Causal Inference Based on an Entropy-Maximization Bayesian Neural Network by Weisi Liu, Xiaogang Pan

“…In this paper, a Bayesian spiking neural network is designed with the entropy-maximization (EM) method to simulate causal inference of visual hidden cues. …”
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Precision in Brief: The Bayesian Hurst–Kolmogorov Method for the Assessment of Long-Range Temporal Correlations in Short Behavioral Time Series by Madhur Mangalam, Aaron D. Likens

“…However, the Bayesian foundation of the HK method fuels reservations about its performance when artifacts corrupt time series. …”
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Efficient Tuning of an Isotope Separation Online System Through Safe Bayesian Optimization with Simulation-Informed Gaussian Process for the Constraints by Santiago Ramos Garces, Ivan De Boi, João Pedro Ramos, Marc Dierckx, Lucia Popescu, Stijn Derammelaere

“…Therefore, a slight modification of safe Bayesian optimization allows for applying the method using a probabilistic classifier for learning classification constraints. …”
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Quality prediction method for automotive body resistance spot welding based on digital twin technology by Ruiping Luo, Shengwen Zhou, Liangyi Nie, Bowen Dong

“…However, traditional prediction methods are limited by the constraints of on-site data collection, which poses challenges to the accuracy of predictions. …”
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Novel Airfoil-Shaped Radar-Absorbing Inlet Grilles on Aircraft Incorporating Metasurfaces: Multidisciplinary Design and Optimization Using EHVI–Bayesian Method by Xufei Wang, Yongqiang Shi, Qingzhen Yang, Huimin Xiang, Saile Zhang

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Distributed robust scheduling of distribution-microgrid based on deep learning method integration by WANG Yihong, LIU Jichun, QIU Gao, ZHOU Hao, HE Peixin

“…Aiming at the problems such as the uncertainty of distributed power output and the low efficiency of operation in the coupled system scheduling of distribution network and microgrid, an optimized scheduling model of Branch-bar operation with chance constraint based on the integration of deep learning method for distribution network and microgrid interconnection system is proposed. …”
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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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Observational Constraints on Basin‐Scale Runoff: A Request for Both Improved ESMs and Streamflow Reconstructions by H. Douville

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CausNet-partial: 'Partial Generational Orderings' based search for optimal sparse Bayesian networks via dynamic programming with parent set constraints. by Nand Sharma, Joshua Millstein

“…In our recent work, we developed a novel dynamic programming algorithm to find optimal Bayesian networks with parent set constraints. This 'generational orderings' based dynamic programming algorithm-CausNet-efficiently searches the space of possible Bayesian networks. …”
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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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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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Constrained Bayesian Optimization: A Review by Sasan Amini, Inneke Vannieuwenhuyse, Alejandro Morales-Hernandez

“…Bayesian optimization is a sequential optimization method that is particularly well suited for problems with limited computational budgets involving expensive and non-convex black-box functions. …”
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