The key idea behind the probabilistic framework to machine learning is that learning can be thought of as inferring plausible models to explain observed data. A machine can use such models to make ...

Ideally, specific treatment for a cancer patient is decided by a multidisciplinary tumor board, integrating prior clinical experience, published data, and patient-specific factors to develop a ...

Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...

Machine Learning to Predict Tamoxifen Nonadherence Among US Commercially Insured Patients With Metastatic Breast Cancer Ideally, specific treatment for a cancer patient is decided by a ...