Bayesian statistics is an approach to data analysis based on Bayes' theorem, where
available knowledge about parameters in a statistical model is updated with the information …

Modern deep learning methods constitute incredibly powerful tools to tackle a myriad of
challenging problems. However, since deep learning methods operate as black boxes, the …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

Until now, much of the work on machine learning and health has focused on processes
inside the hospital or clinic. However, this represents only a narrow set of tasks and …

This article compares two missing data procedures, full information maximum likelihood
(FIML) and multiple imputation (MI), to investigate their relative performances in relation to …

Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …

The twenty-first century has seen a breathtaking expansion of statistical methodology, both
in scope and influence.'Data science'and'machine learning'have become familiar terms in …

The paper examines the opportunities in and possibilities arising from big data in retailing,
particularly along five major data dimensions—data pertaining to customers, products …

The Internet of Medical Things (IoMT) has a bright future with the development of smart
mobile devices. Information technology is also leading changes in the healthcare industry …