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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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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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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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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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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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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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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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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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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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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Determination of Maximum Bayesian Entropy Probability Distribution

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Cognitive biases as Bayesian probability weighting in context by Bruno Kopp

“…By integrating normative Bayesian principles with psychological insights, the ABC model recasts cognitive biases as adaptive strategies shaped by capacity constraints and meta-learning in specific contexts. …”
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Quantum Bayesian Inference with Renormalization for Gravitational Waves by Gabriel Escrig, Roberto Campos, Hong Qi, M. A. Martin-Delgado

“…In this work, we introduce a hybrid quantum algorithm qBIRD , which performs quantum Bayesian inference with renormalization and downsampling to infer GW parameters. …”
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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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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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