Certain classes of Bayesian hierarchical models have shown to be particularly useful in such contexts. Bayesian approaches are strongly connected to statistical computational methods, and in ...

Certain classes of Bayesian hierarchical models have shown to be particularly useful in such contexts. Bayesian approaches are strongly connected to statistical computational methods, and in ...

While Bayesian deep learning methods have shown promise for uncertainty estimation, most research has focused on single-label tasks. Researchers from the Institute of Automation, Chinese Academy of ...

Then, a sparsity mechanism is proposed to automatically fuse these candidate degradation models based on the sparse variational Bayesian method. As a result, we can find the most appropriate ...

To address these challenges, a Bayesian optimization framework was proposed herein as a solution. Surrogate models, including two Gaussian process-based and five non-Gaussian process-based models, are ...

The presented case studies provide convincing evidence of the superiority of the proposed methodology to existing approaches, which could promote the use of Bayesian statistics among neuroscientists.

It subsequently utilizes the DeepTXinferrer module to accurately infer potential transcriptional burst kinetic parameters using Bayesian methods. DeepTX demonstrates good performance on synthetic ...

A major breakthrough in A/B testing infrastructure has led to a significant boost in operational efficiency, with testing ...

Significant advancements in understanding the molecular and cellular mechanisms of tumor progression have been made, yet ...

SHR-A1811 showed promising antitumor activity and acceptable tolerability in patients with HER2-positive breast cancer, ...