Results 31 to 40 of about 50,759 (165)
bamdit: An R Package for Bayesian Meta-Analysis of Diagnostic Test Data
Journal of Statistical Software, 2018 In this paper we present the R package bamdit. The name of the package stands for "Bayesian meta-analysis of diagnostic test data". bamdit was developed with the aim of simplifying the use of models in meta-analysis, that up to now have demanded great ...
Pablo Emilio Verde
doaj +1 more source
Barekeng, 2022 Cervical cancer is the most common cancer that causes death in women. This cancer is mainly caused by Human Papilloma Virus (HPV). It is estimated that 52 million of Indonesian women are at risk of having cancer, and 36% of female cancer patients suffer ...
Nur Mahmudah, Fetrika Anggraini
doaj +1 more source
An improved method of groundwater model structural uncertainty analysis
Shuiwen dizhi gongcheng dizhi, 2021 Gaussian Process Regression (GPR) is a supervised learning algorithm based on Bayesian theory, which is widely used in model structural uncertainty analysis based on data-driven method (DDM).
Xiaozhuo SUN +3 more
doaj +1 more source
Orthogonal MCMC algorithms [PDF]
2014 IEEE Workshop on Statistical Signal Processing (SSP), 2014 Monte Carlo (MC) methods are widely used in signal processing, machine learning and stochastic optimization. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce a novel parallel interacting MCMC scheme, where the parallel chains share information using another MCMC technique working on the entire ...
Luca Martino +4 more
openaire +4 more sources
Archives of Pharmaceutical Sciences Ain Shams University, 2022 Renal Organic Cation Transporter 2 (OCT2) plays a major role in metformin elimination. Daclatasvir, a Direct-Acting Antiviral (DAA), is an OCT2 inhibitor.
Mohamed Raslan +2 more
doaj +1 more source
Revisiting Bayesian Autoencoders With MCMC
IEEE Access, 2022 Autoencoders gained popularity in the deep learning revolution given their ability to compress data and provide dimensionality reduction. Although prominent deep learning methods have been used to enhance autoencoders, the need to provide robust uncertainty quantification remains a challenge. This has been addressed with variational autoencoders so far.
Rohitash Chandra +3 more
openaire +4 more sources
Evaluating the Usability of a Medical Care Monitoring Center System Using Heuristic Method [PDF]
مجله انفورماتیک سلامت و زیست پزشکی, 2023 Introduction: Simultaneously with the COVID-19 epidemic, many efforts were made to manage and treat the disease. One of the main activities was the development of health information systems to help monitor this disease, but due to the speed of the ...
Zohreh Hashemi +3 more
doaj
Aquifer hydraulic conductivity prediction via coupling model of MCMC-ANN
Journal of Groundwater Science and Engineering, 2021 Grain-size distribution data, as a substitute for measuring hydraulic conductivity (K), has often been used to get K value indirectly. With grain-size distribution data of 150 sets of samples being input data, this study combined the Artificial Neural ...
Chun-lei GUI +3 more
doaj +1 more source
Multilevel Delayed Acceptance MCMC
SIAM/ASA Journal on Uncertainty Quantification, 2023 29 pages, 12 ...
Mikkel Bue Lykkegaard +4 more
openaire +2 more sources
Spbsampling: An R Package for Spatially Balanced Sampling
Journal of Statistical Software, 2022 The basic idea underpinning the theory of spatially balanced sampling is that units closer to each other provide less information about a target of inference than units farther apart.
Francesco Pantalone +2 more
doaj +1 more source