Enhancing catch-based stock assessment in data-limited fisheries with proxy CPUE indicators in the Yellow Sea by Kun Wang, Qi Li, Chongliang Zhang, Binduo Xu, Yiping Ren

“…These indicators were incorporated into a Bayesian state-space Schaefer surplus production model (BSM) and their performance was compared to catch-only methods (CMSY) across key evaluation criteria, including robustness of estimation, reliability in retrospective analyses, and performance when encountering catch observation errors. …”
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Shale Fracture Static Stability Evaluation Method Based on 3D Discrete Fracture Model: A Case Study on the Luzhou Area in Southern Sichuan Basin, SW China by Ruhua Zhang, Cheng Yin, Jianping Huang

“…In this article, seismic geometric attributes were calculated, and fracture seismic facies was established by means of the Bayesian probabilistic cluster analysis method. Then, 3D discrete fracture modeling under the constraint of fracture seismic facies was performed to establish a discrete fracture model. …”
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Comparative Analysis of Machine Learning Models for Predicting Innovation Outcomes: An Applied AI Approach by Marko Martinović, Kristian Dokic, Dalibor Pudić

“…In this study, multiple machine learning models, encompassing both ensemble-based and single-model approaches, were applied to data from the Community Innovation Survey. Methods included random forests, gradient boosting frameworks, support vector machines, neural networks, and logistic regression, each with hyperparameters optimized through Bayesian search routines and evaluated using corrected cross-validation techniques. …”
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The rarity of mutations and the inflation of bacterial effective population sizes by Rui Borges

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An Entropy Dynamics Approach to Inferring Fractal-Order Complexity in the Electromagnetics of Solids by Basanta R. Pahari, William Oates

“…The approach uses an information-theoretic method by combining Shannon’s entropy with fractional moment constraints in time and space. …”
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Improving the Minimum Free Energy Principle to the Maximum Information Efficiency Principle by Chenguang Lu

“…Friston proposed the Minimum Free Energy Principle (FEP) based on the Variational Bayesian (VB) method. This principle emphasizes that the brain and behavior coordinate with the environment, promoting self-organization. …”
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Virtual reality-augmented differentiable simulations for digital twin applications in surgical planning by Robin Cremese, Charlotte Godard, Alexis Bénichou, Thomas Blanc, Iwan Quemada, Christian L. Vestergaard, Bassam Hajj, Alex Barbier–Chebbah, François Laurent, Jean-Baptiste Masson

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Comment on “Opinion: Can uncertainty in climate sensitivity be narrowed further?” by Sherwood and Forest (2024) by N. Lewis

“…This comment also discusses the role of priors in Bayesian ECS estimation and explains why the subjective Bayesian approach favoured by SF24 can often produce unreliable inference for uncertain parameters such as ECS. …”
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Data-driven discovery and parameter estimation of mathematical models in biological pattern formation. by Hidekazu Hishinuma, Hisako Takigawa-Imamura, Takashi Miura

“…This method allows for parameter estimation under minimal constraints; i.e., it does not require time-series data or initial conditions and is applicable to various types of mathematical models. …”
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Intelligent Clustering and Adaptive Energy Management in Wireless Sensor Networks with KDE-Based Deployment by Mainak Kundu, Ria Kanjilal, Ismail Uysal

“…Simulation results demonstrate significant improvements in performance, including over 35% extension in network lifetime and higher coverage retention under energy constraints, compared to baseline methods such as LEACH and K-LEACH. …”
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Normalizing flow-assisted nested sampling on Type-II Seesaw model by Rajneil Baruah, Subhadeep Mondal, Sunando Kumar Patra, Satyajit Roy

“…We present the results of our detailed Bayesian exploration of the model parameter space subjected to theoretical constraints and experimental data corresponding to the 125 GeV Higgs boson, $$\rho $$ ρ -parameter, and the oblique parameters. …”
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Deterministic and statistical calibration of constitutive models from full-field data with parametric physics-informed neural networks by David Anton, Jendrik-Alexander Tröger, Henning Wessels, Ulrich Römer, Alexander Henkes, Stefan Hartmann

“…However, monitoring is usually associated with severe time constraints, difficult to meet with standard numerical approaches. …”
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Genetic Strategies for Enhancing Rooster Fertility in Tropical and Humid Climates: Challenges and Opportunities by Jiraporn Juiputta, Vibuntita Chankitisakul, Wuttigrai Boonkum

“…We addressed the trends and scientific developments in male chicken genetic selection, together with the benefits and constraints of each method. This will help breeders and researchers to create the most successful genetic selection plans for the next generation of chickens.…”
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Stereo Online Self-Calibration Through the Combination of Hybrid Cost Functions with Shared Characteristics Considering Cost Uncertainty by Wonju Lee

“…In this work, we propose a markerless method for obtaining stereo extrinsic calibration by employing nonlinear optimization on a manifold, which leverages the inherent observability property. …”
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Using the Mid‐Holocene “Greening” of the Sahara to Narrow Acceptable Ranges on Climate Model Parameters by Peter O. Hopcroft, Paul J. Valdes, William Ingram

“…One possible explanation is the presence of systematic biases in the representations of atmospheric convection which might also impact future projections. We employ a Bayesian method to learn from an ensemble of present day and mid‐Holocene simulations that vary parameters in the convection, boundary layer and cloud schemes. …”
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Evaluation of prior probability distribution of undrained cohesion for soil in Nasiriyah by Ressol Shakir

“…It was concluded that Jeffreys method is used well with individual models at the mean value of cohesion of 28.66 kPa and the standard deviation of 1.19 kPa. …”
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Unconstrained Metropolis–Hastings Sampling of Covariance Matrices by Daniel Turek

“…Markov chain Monte Carlo (MCMC), the predominant algorithm for fitting hierarchal models to data in a Bayesian setting, relies on the ability to sample from the full conditional distributions of unobserved parameters. …”
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A Federated Learning Model for Detecting Cyberattacks in Internet of Medical Things Networks by Abdallah Ghourabi, Adel Alkhalil

“…The XGBoost models are further optimized using a Bayesian method and integrated with an aggregation algorithm to construct an adaptive global model. …”
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Managing Maintenance Backlogs: An Integrated Multi-Criteria Decision-Making and Optimization Approach by Ehsan Esmaeeli, Mohsen Varmazyar, Vahid Hekmatshoar, Parviz Boroomandfar, Mohammad Reza Feylizadeh

“…The current study presents a comprehensive framework that integrates the Decision-Making Trial and Evaluation Laboratory (DEMATEL), the Bayesian Best-Worst Method (BWM), and a multi-dimensional knapsack optimization model to address maintenance backlog management challenges. …”
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Toward reliable fluorescence imaging: Optical prior-guided probabilistic reconstruction for structured illumination microscopy by Kun Lin, Junkang Dai, Huaian Chen, Yi Jin

“…Here we present PG-SIM, a probabilistic SIM reconstruction method based on Bayesian neural networks and incorporating graph representation learning (GRL) to model optical prior knowledge. …”
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