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Proximal 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 PDFEmpowering professionals: a multidisciplinary approach to supporting people seeking asylum and refugees.
BMJ Paediatr Open
December 2024
Community Child Health, Royal Free London NHS Foundation Trust Children's Services, London, UK
People seeking asylum and refugees living in initial accommodation in the UK can experience significant barriers to accessing care. They often have complex health and social needs and may experience fragmentation in the delivery of services. The Respond Integrated Refugee Health Service, hosted by University College London Hospital and operating across North Central London, has been created to address some of these challenges.
View Article and Find Full Text PDFRadiomics and deep learning models for glioblastoma treatment outcome prediction based on tumor invasion modeling.
Phys Med
December 2024
Division of Medical Radiation Physics, Department of Physics, Stockholm University, Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
Purpose: We investigate the feasibility of using a biophysically guided approach for delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by evaluating its impact on the treatment outcomes, specifically Overall Survival (OS) time.
Methods: An established reaction-diffusion model was employed to simulate the spatiotemporal evolution of cancerous regions in T1-MRI images of GBM patients. The effects of the parameters of this model on the simulated tumor borders were quantified and the optimal values were used to estimate the distribution of infiltrative cells (CTVmodel).
The development of a waste management and classification system based on deep learning and Internet of Things.
Environ Monit Assess
December 2024
Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China.
Waste sorting is a key part of sustainable development. To maximize the recovery of resources and reduce labor costs, a waste management and classification system is established. In the system, we use Internet of Things (IoT) and edge computing to implement waste sorting and the systematic long-distance information transmission and monitoring.
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