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 ...

When we use simulation to estimate the performance of a stochastic system, the simulation often contains input models that were estimated from real-world data; therefore, there is both simulation and ...

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 ...

Regular Bayesian and frequentist approximations in statistics are studied within a unified framework. In particular it is shown how some higher-order likelihood-based approximations arise from their ...

In a world of uncertainty and shifting narratives, this post proposes a new model for investing: Bayesian edge investing. Unlike modern portfolio theory, which assumes equilibrium and perfect ...

A research team introduces a hierarchical Bayesian spatial approach that integrates UAV and terrestrial LiDAR data to ...