Spike-Based Bayesian-Hebbian Learning of Temporal Sequences. by Philip J Tully, Henrik Lindén, Matthias H Hennig, Anders Lansner

“…To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. …”
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Bayesian Computation Methods for Inference in Stochastic Kinetic Models by Eugenia Koblents, Inés P. Mariño, Joaquín Míguez

“…Recently, it has been shown that an alternative approach to Bayesian computation, namely, the class of adaptive importance samplers, may be more efficient than classical MCMC-like schemes, at least for certain applications. …”
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Marine motor fault diagnosis based on CEEMDAN and BRECAN under strong noise conditions by Renjie ZHU, Enzhe SONG, Chong YAO, Yun KE

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Fish Community Resource Utilization Reveals Benthic–Pelagic Trophic Coupling Along Depth Gradients in the Beibu Gulf, South China Sea by Xiaodong Yang, Konglan Luo, Jiawei Fu, Bin Kang, Xiongbo He, Yunrong Yan

“…To better understand the energy coupling of consumers between coastal marine habitats, this study employed a Bayesian mixture model using SC and SI data. By classifying functional groups based on taxonomy, morphological traits, and feeding ecology similarities, we constructed a trophic network and analyzed the changes in fish feeding patterns and the dynamics of benthic–pelagic coupling across environmental gradients. …”
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Bayesian joint-regression analysis of unbalanced series of on-farm trials by Turbet Delof, Michel, Rivière, Pierre, Dawson, Julie C, Gauffreteau, Arnaud, Goldringer, Isabelle, van Frank, Gaëlle, David, Olivier

“…To explore methods of overcoming these challenges, this article tests various data analysis scenarios using a Bayesian approach with different models and a real wheat PPB dataset over 11 years. …”
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A MACHINE LEARNING DISTRACTED DRIVING PREDICTION MODEL by Samira AHANGARI, Mansoureh JEIHANI, Abdollah DEHZANGI

“…Detecting driver distraction would help in adapting the most effective countermeasures. To find the best strategies to overcome this problem, we developed a Bayesian Network (BN) distracted driving prediction model using a driving simulator. …”
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Adaptive trial for the treatment of depressive symptoms associated with concussion using accelerated intermittent theta burst stimulation (ADEPT): rationale, design and methods by Lindsay M. Oberman, Adriana I. Penafiel, Rachel Dieterich, Cuong T. Phan, Yi-Yu Chou, Dzung L. Pham, Maheen M. Adamson, Maheen M. Adamson, Christopher E. Hines, Zeynab Rezaee, Zhi-De Deng, Hemant Pal, Sarah H. Lisanby, Sarah H. Lisanby, David L. Brody, David L. Brody

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A review of climate change impact assessment and methodologies for urban sewer networks by Amir Masoud Karimi, Mostafa Babaeian Jelodar, Teo Susnjak, Monty Sutrisna

“…By integrating these uncertainties with a Bayesian Network, which can incorporate expert opinion, failure probabilities are modelled based on variable interactions, improving prediction.The study also emphasises the importance of factors, such as urbanisation, asset deterioration, and adaptation programs in order to improve predictive accuracy. …”
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An Adaptive Graph Convolutional Network with Spatial Autocorrelation for Enhancing 3D Soil Pollutant Mapping Precision from Sparse Borehole Data by Huan Tao, Ziyang Li, Shengdong Nie, Hengkai Li, Dan Zhao

“…Traditional interpolation methods may obscure local variations in soil contamination when applied to such sparse data, thus reducing the interpolation accuracy. We propose an adaptive graph convolutional network with spatial autocorrelation (ASI-GCN) model to overcome this challenge. …”
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Predictive Analytics by Using Bayesian Model Averaging for Large-Scale Internet of Things by Xinghui Zhu, Fang Kui, Yongheng Wang

“…This method, which is based on a new multilayered adaptive dynamic Bayesian network model, uses Gaussian mixture models and expectation-maximization inference for basic Bayesian prediction. …”
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Comparative effectiveness of Omicron XBB 1.5-adapted COVID-19 vaccines: a systematic literature review and network meta-analysis by Kyle Fahrbach, Allie Cichewicz, Haitao Chu, Manuela Di Fusco, Heather Burnett, Hannah R. Volkman, Morodoluwa Akin-Fajiye, Carlos Fernando Mendoza, Joseph C. Cappelleri

“…We conducted a systematic review and network meta-analysis (NMA) feasibility assessment of effectiveness studies of Omicron-adapted COVID-19 vaccines.Research design and methods Searches in MEDLINE and Embase up to February 2025 identified studies comparing the effectiveness of Omicron-adapted COVID-19 vaccines, either directly or against no recent vaccine. …”
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HEURISTIC SYNTHESIS ALGORITHM OF EVENT DEPENDENCIES MODEL by Dmitry V. Gorishny

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Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines by Enamundram Naga Karthik, Farida Cheriet, Catherine Laporte

“…Uncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks' behavior. …”
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Charting new territories: fuzzy systems in English language teaching and learning by Xiaomei Wen, Deng Pan

“…It is built in this experimental research employing AI methodologies based on fuzzy logic and the Bayesian network methodology. Using conventional approaches that rely primarily on numerical scores to evaluate academics’ teaching and research activities at various levels is becoming increasingly challenging. …”
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A probabilistic neural network-based bimanual control method with multimodal haptic perception fusion by Xinrui Chi, Zhanbin Guo, Fu Cheng

“…A hierarchical heterogeneous feature alignment (HHFA) module is designed to solve the spatio-temporal asynchrony of multi-source signals (root mean square error <0.8 ms), and a dynamic Bayesian fusion layer (DBFL) is developed to achieve adaptive weighting based on the entropy-variance coupling index, suppressing noise interference and modal conflicts. …”
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Building resilience against physical risk: the role of logistics performance in risk mitigation by Abroon Qazi

“…This study examines the critical role of logistics performance in building resilience against physical risk, utilizing the FM Global Resilience Index—a composite measure of countries’ relative enterprise resilience to disruptive events—as a key benchmark. By applying a Bayesian Belief Network (BBN) modeling approach, data from 130 countries are analyzed, focusing on key risk factors such as climate risk quality, fire risk quality, and cybersecurity. …”
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Assessing Construction Safety Performance in Urban Underground Space Development Projects from a Resilience Enhancement Perspective by Xiaohua Yang, Xiaer Xiahou, Kang Li, Qiming Li

“…Utilizing a large-scale underground construction project as a case study, the Bayesian network inference technique is applied to ascertain the project’s safety resilience value. …”
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Three-Dimensional CKANs: UUV Noncooperative Target State Estimation Approach Based on 3D Convolutional Kolmogorov–Arnold Networks by Changjian Lin, Dan Yu, Shibo Lin

“…The incorporation of the Kolmogorov–Arnold representation within the convolutional layers enhances the network’s capacity for nonlinear expression and adaptability in processing spatial information. …”
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Bayesian optimized deep learning and ensemble classification approach for multiclass plant disease identification by Silpa Padmanabhuni, Pradeepini Gera

“…Bayesian optimization is used to identify and combine optimal activation functions, enhancing the network's capacity to learn complex disease patterns from tomato leaf images. …”
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Forecasting Magnitude and Frequency of Seasonal Streamflow Extremes Using a Bayesian Hierarchical Framework by Álvaro Ossandón, Balaji Rajagopalan, William Kleiber

“…Abstract We develop a space‐time Bayesian hierarchical modeling (BHM) framework for two flood risk attributes—seasonal daily maximum flow and the number of events that exceed a threshold during a season (NEETM)—at a suite of gauge locations on a river network. …”
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