We apply a linear Bayesian model to seismic tomography, a high-dimensional inverse problem in geophysics. The objective is to estimate the three-dimensional structure ...

In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to ...

This uncertainty primarily arises from the limitations in modeling gravitational wave signals. Just as accurately determining the location of an earthquake source requires precise models of the ...

Dr. James McCaffrey of Microsoft Research says the main advantage of using Gaussian naive Bayes classification compared to other techniques like decision trees or neural networks is that you don't ...

Stochastic dynamical systems arise in many scientific fields, such as asset prices in financial markets, neural activity in ...

The core of this patent lies in utilizing large models to handle multimodal data conflicts in chemical HSE scenarios. The technical route can be summarized in several key steps: First, collect ...

A new study published in JCO Clinical Cancer Informatics demonstrates that machine learning models incorporating patient-reported outcomes and wearable sensor data can predict which patients ...

A study published in JCO Clinical Cancer Informatics demonstrates that machine learning models incorporating patient-reported ...