Identifying structures of complex networks based on time series of nodal data is of considerable interest and significance in many fields of science and engineering. This article presents a sparse Bayesian learning (SBL) method for identifying structures of community-bridge networks, where nodes are grouped to form communities connected via bridges. Using the structural information of such networks with unknown nodal dynamics and community formations, network structure identification is tackled similar to sparse signal reconstruction with mixed sparsity mode. The proposed method is theoretically proved to be convergent. Its superiority to mainstream baselines is demonstrated via extensive experiments without the need for manual adjustment of regularization parameters.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/TNNLS.2024.3412870 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
De-biasing the bias: methods for improving disparity assessments with noisy group measurements.
Biometrics
October 2024
RAND Corporation, Pittsburgh, PA 15213, United States.
Health care decisions are increasingly informed by clinical decision support algorithms, but these algorithms may perpetuate or increase racial and ethnic disparities in access to and quality of health care. Further complicating the problem, clinical data often have missing or poor quality racial and ethnic information, which can lead to misleading assessments of algorithmic bias. We present novel statistical methods that allow for the use of probabilities of racial/ethnic group membership in assessments of algorithm performance and quantify the statistical bias that results from error in these imputed group probabilities.
View Article and Find Full Text PDFA new prediction model based on deep learning for pig house environment.
Sci Rep
December 2024
School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar, 161006, China.
A prediction model of the pig house environment based on Bayesian optimization (BO), squeeze and excitation block (SE), convolutional neural network (CNN) and gated recurrent unit (GRU) is proposed to improve the prediction accuracy and animal welfare and take control measures in advance. To ensure the optimal model configuration, the model uses a BO algorithm to fine-tune hyper-parameters, such as the number of GRUs, initial learning rate and L2 normal form regularization factor. The environmental data are fed into the SE-CNN block, which extracts the local features of the data through convolutional operations.
View Article and Find Full Text PDFComparative evaluation of spatiotemporal methods for effective dengue cluster detection with a case study of national surveillance data in Thailand.
Sci Rep
December 2024
Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Dengue fever poses a significant public health burden in tropical regions, including Thailand, where periodic epidemics strain healthcare resources. Effective disease surveillance is essential for timely intervention and resource allocation. Various methods exist for spatiotemporal cluster detection, but their comparative performance remains unclear.
View Article and Find Full Text PDFProximal MCMC for Bayesian Inference of Constrained and Regularized Estimation.
Am Stat
February 2024
Department of Biostatistics, UCLA.
This paper advocates proximal Markov Chain Monte Carlo (ProxMCMC) as a flexible and general Bayesian inference framework for constrained or regularized estimation. Originally introduced in the Bayesian imaging literature, ProxMCMC employs the Moreau-Yosida envelope for a smooth approximation of the total-variation regularization term, fixes variance and regularization strength parameters as constants, and uses the Langevin algorithm for the posterior sampling. We extend ProxMCMC to be fully Bayesian by providing data-adaptive estimation of all parameters including the regularization strength parameter.
View Article and Find Full Text PDFDynamic Bayesian network modeling for longitudinal data on child undernutrition in Ethiopia (2002-2016).
Front Public Health
December 2024
Department of Statistics, College of Science, Bahir Dar University, Bahir Dar, Ethiopia.
Introduction: Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002-2016 was used to analyze the complex relationships among various covariates influencing child undernutrition.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!